https://ai-solutions.wiki/patterns/Recent content in AI Patterns on AI Solutions WikiHugoen-usSat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ab-testing-ml/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ab-testing-ml/A/B testing ML models is fundamentally different from A/B testing UI changes. Model outputs are probabilistic, effects can be subtle, and the interaction between model behavior and user behavior creates feedback loops that confuse naive analysis. Getting A/B testing right for ML requires careful experimental design. Traffic Splitting How you split traffic between model variants matters more than you think. User-level splitting - Each user is consistently assigned to one variant for the duration of the test.https://ai-solutions.wiki/patterns/ai-audit-trail/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ai-audit-trail/Regulators, auditors, and internal governance teams need to answer a specific question: why did the AI system make this decision? An audit trail provides the answer by capturing an immutable record of every input, output, model version, configuration, and intermediate step involved in each AI-driven decision. What to Capture Request context - The full input to the model, including system prompt, user message, retrieved context (for RAG systems), and any tool outputs consumed.https://ai-solutions.wiki/patterns/ai-gateway-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ai-gateway-pattern/Every team that integrates more than one AI model provider eventually builds the same thing: a proxy layer that handles authentication, logging, retries, and cost tracking. The AI gateway pattern formalizes this into a dedicated infrastructure component that sits between your application code and all external model APIs. Why a Gateway Without a gateway, each service that calls an LLM implements its own retry logic, its own API key management, its own usage tracking.https://ai-solutions.wiki/patterns/ai-supply-chain-security/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ai-supply-chain-security/AI systems depend on artifacts that traditional software supply chain security does not cover: pretrained model weights, tokenizer files, embedding models, dataset snapshots, and specialized inference runtimes. A compromised model weight file can introduce backdoors that are invisible to standard code review. AI supply chain security extends software supply chain practices to cover these AI-specific artifacts. Attack Surface Poisoned model weights - An attacker modifies pretrained weights to introduce a backdoor that activates on specific trigger inputs.https://ai-solutions.wiki/patterns/ai-system-decommissioning/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ai-system-decommissioning/Every AI system has a finite useful life. Models degrade as data distributions shift. Regulations change. Better alternatives emerge. Yet most organizations invest heavily in deploying AI systems and give almost no thought to retiring them. The decommissioning pattern provides a structured approach to sunsetting AI systems safely, preserving compliance artifacts, and avoiding disruption to dependent services. Origins and History System decommissioning as a formal practice emerged from IT asset management and enterprise architecture disciplines in the 1990s, when organizations began grappling with legacy system retirement during Y2K remediation and ERP migrations [1].https://ai-solutions.wiki/patterns/test-pyramid-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/test-pyramid-ai/The traditional test pyramid (many unit tests, fewer integration tests, fewest E2E tests) applies to AI systems but needs an additional layer: evaluation tests that validate model output quality. The AI test pyramid has four layers, each with distinct characteristics. Layer 1: Unit Tests (Deterministic Logic) What they test: Prompt template rendering, output parsers, input validators, chunking functions, embedding preprocessing, configuration loading, error handling, and all other deterministic code. Characteristics:https://ai-solutions.wiki/patterns/audio-transcription-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/audio-transcription-pipeline/Audio transcription converts speech to text, but a production transcription pipeline needs much more than a single API call. Pre-processing handles audio quality issues, diarization identifies speakers, and post-processing adds punctuation, formatting, and domain-specific corrections. Pre-Processing Raw audio often needs cleanup before transcription for optimal results. Format normalization - Convert audio to the format expected by the transcription service (typically WAV or FLAC at 16kHz mono). Multi-channel audio should be mixed to mono unless per-channel processing is desired (e.https://ai-solutions.wiki/patterns/automated-compliance-monitoring/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/automated-compliance-monitoring/Manual compliance checks do not scale with the pace of AI development. This pattern describes an automated compliance monitoring architecture that continuously evaluates AI systems against regulatory requirements and organizational policies. Pattern Overview A compliance monitoring platform ingests signals from AI infrastructure, model registries, data pipelines, and security tools. It evaluates these signals against codified compliance rules and generates alerts, reports, and audit trails. The platform operates continuously, not as a periodic audit.https://ai-solutions.wiki/patterns/batch-inference/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/batch-inference/Not all AI workloads need real-time responses. Processing a backlog of documents, analyzing historical data, or generating reports for all customers are batch workloads where throughput and cost matter more than latency. Batch inference patterns optimize for these priorities. Queue-Based Batch Processing The foundational pattern for batch inference. Work items are placed in a queue, and workers pull items, process them through the model, and write results to storage. Queue design - Use a managed queue service (SQS, RabbitMQ) with visibility timeout set to the maximum expected processing time per item.https://ai-solutions.wiki/patterns/chain-of-thought/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/chain-of-thought/Chain-of-thought (CoT) prompting instructs the model to show its reasoning step by step before producing a final answer. Instead of jumping directly to a conclusion, the model works through the problem explicitly. This simple technique significantly improves accuracy on math, logic, multi-step reasoning, and complex analysis tasks. The Core Technique The simplest form of CoT is adding “Think step by step” or “Show your reasoning” to the prompt. The model then generates intermediate reasoning steps before the final answer.https://ai-solutions.wiki/patterns/compliance-as-code/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/compliance-as-code/AI systems operate under increasing regulatory scrutiny. The EU AI Act, GDPR, CCPA, industry-specific regulations (HIPAA, SOX, PCI-DSS), and emerging AI-specific legislation impose requirements on data handling, model transparency, bias monitoring, and audit trails. Manual compliance processes - spreadsheet checklists, periodic audits, documented reviews - do not scale with the pace of AI development. Compliance as code encodes regulatory requirements as automated checks that run continuously in CI/CD pipelines and production environments.https://ai-solutions.wiki/patterns/continuous-training-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/continuous-training-pattern/Models trained once on a static dataset become stale as the world changes. Customer behavior shifts, product catalogs update, and seasonal patterns emerge. Continuous training automates the retraining cycle so that models stay current without requiring an engineer to manually trigger each training run, evaluate results, and promote the new version. Trigger Strategies Scheduled retraining - Train on a fixed cadence (daily, weekly, monthly) regardless of whether drift has been detected.https://ai-solutions.wiki/patterns/data-contract-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/data-contract-pattern/In a microservices architecture, data flows between teams. The user activity team produces clickstream data. The ML team consumes it for recommendation model training. The analytics team uses it for reporting. When the user activity team renames a field, both downstream teams break. The data contract pattern makes these dependencies explicit and prevents breaking changes from reaching consumers. The Pattern A data contract is a versioned, machine-readable specification that defines:https://ai-solutions.wiki/patterns/data-flywheel/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/data-flywheel/The data flywheel is the most powerful long-term advantage in applied AI. The cycle works like this: a model serves users, users generate interaction data, that data improves the model, the improved model attracts more users, and those users generate more data. Each revolution of the flywheel makes the next one faster. The Flywheel Mechanics Serve - The model handles production requests. Every interaction produces data: the input, the output, and the user’s reaction to the output.https://ai-solutions.wiki/patterns/data-product-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/data-product-pattern/Most organizational data is managed as a byproduct of operational systems. Tables are created as implementation details of applications, poorly documented, and governed informally. Downstream consumers (analysts, ML engineers, other teams) reverse-engineer schemas, guess at semantics, and build pipelines on unstable foundations. The data product pattern treats each shared dataset as a product with defined consumers, quality guarantees, and an accountable owner. What Makes Data a Product A dataset becomes a data product when it has five properties:https://ai-solutions.wiki/patterns/data-versioning/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/data-versioning/ML experiments are only reproducible when both code and data are versioned. Git tracks code changes, but datasets are too large for Git and change in ways that code versioning does not capture: rows are added, labels are corrected, features are recomputed, and filtering criteria change. Data versioning applies version control concepts to datasets so that any experiment can be reproduced by checking out the exact data version used. Why Data Versioning Matters Without data versioning, teams cannot answer basic questions.https://ai-solutions.wiki/patterns/differential-privacy-ml/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/differential-privacy-ml/ML models memorize training data. Large language models can reproduce verbatim passages from their training corpus. Classification models leak information about whether a specific individual was in the training set. Differential privacy provides a mathematical framework for training models that learn statistical patterns from a dataset without memorizing information about any individual record. The Core Guarantee A training algorithm satisfies (epsilon, delta)-differential privacy if the probability of any particular model output changes by at most a factor of e^epsilon when any single training example is added or removed.https://ai-solutions.wiki/patterns/direct-model-interface/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/direct-model-interface/The direct model interface is the most basic AI integration pattern. User input is sent to a model API. The model generates a response. The response is returned to the user. No chains, no agents, no tools, no orchestration. One input, one output, one model call. This pattern is underrated. Teams often jump to complex agentic architectures when a direct model call with a well-crafted system prompt solves the problem. Start here and add complexity only when you hit a concrete limitation.https://ai-solutions.wiki/patterns/document-classification/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/document-classification/Document classification assigns one or more labels to a document based on its content. It is among the most common AI tasks in enterprise applications: routing incoming correspondence, categorizing support tickets, tagging content for search, and classifying documents for compliance. Classification Approaches Zero-shot classification - Use an LLM to classify documents without task-specific training data. Provide the category definitions in the prompt and ask the model to assign the most appropriate category.https://ai-solutions.wiki/patterns/edge-mlops/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/edge-mlops/Deploying ML models to edge devices introduces constraints that cloud-based MLOps pipelines do not account for. Edge devices have limited compute, memory, and storage. Network connectivity is intermittent or absent. Thousands of heterogeneous devices must be updated safely. Edge MLOps adapts the ML lifecycle to these constraints. Model Optimization Pipeline Models trained in the cloud must be optimized before edge deployment. The optimization pipeline includes multiple stages, each reducing model size and computational requirements.https://ai-solutions.wiki/patterns/embedding-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/embedding-pipeline/Embeddings convert text, images, or other data into dense vector representations that capture semantic meaning. An embedding pipeline handles the full lifecycle: chunking source content, generating embeddings, storing them in a vector database, and querying them for retrieval. Getting each stage right is critical for downstream quality, especially in RAG systems. Chunking Strategy How you split source documents into chunks determines retrieval quality more than any other factor. Fixed-size chunking - Split text into chunks of N tokens with overlap.https://ai-solutions.wiki/patterns/entity-extraction/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/entity-extraction/Entity extraction pulls structured information from unstructured text: names, dates, amounts, organizations, locations, and domain-specific entities. It is the bridge between document AI and downstream business systems that need structured data. Schema-Driven Extraction Define the expected output schema explicitly and instruct the model to populate it. Implementation - Provide the model with a target schema (JSON schema, data class definition, or structured description of expected fields) and the source text. The model extracts values for each field.https://ai-solutions.wiki/patterns/evaluator-optimizer/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/evaluator-optimizer/LLM outputs vary in quality. A single generation may miss requirements, contain errors, or fail to match the desired format. The evaluator-optimizer pattern addresses this by introducing an automated quality loop: a generator produces output, an evaluator scores it against criteria, and if the score falls below a threshold, the generator tries again with feedback from the evaluator. The Loop Generate - The generator model produces an initial output based on the user’s request and any provided context.https://ai-solutions.wiki/patterns/explainability-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/explainability-pattern/Explainability patterns make AI decision-making transparent to the people affected by those decisions. When an AI system denies a loan, flags content for removal, or recommends a medical treatment, the people involved need to understand why. Regulators increasingly require it. Explainability is not a feature you bolt on after deployment - it is an architectural pattern that must be designed in from the start. Levels of Explainability System-level - How does the overall system work?https://ai-solutions.wiki/patterns/explainability-service/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/explainability-service/Regulators ask why a model made a specific decision. Customers ask why their loan was denied. Internal reviewers ask which features drove a risk score. An explainability service provides on-demand explanations for individual predictions, decoupled from the model serving infrastructure so that explanation generation does not impact inference latency. Why a Dedicated Service Computing explanations is expensive. SHAP values require hundreds or thousands of model evaluations per explanation. LIME fits a local surrogate model for each instance.https://ai-solutions.wiki/patterns/fallback-chain/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/fallback-chain/Model APIs go down. Rate limits get hit. Responses come back garbled. A production AI system that depends on a single model provider is one outage away from a complete service failure. The fallback chain pattern defines an ordered sequence of alternative models that the system tries when the primary model is unavailable or produces unacceptable results. How It Works A fallback chain is an ordered list of model configurations. The system tries the first model in the chain.https://ai-solutions.wiki/patterns/fan-out-fan-in-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/fan-out-fan-in-ai/Sequential LLM calls are slow. When a task can be decomposed into independent subtasks, running them in parallel dramatically reduces end-to-end latency. The fan-out/fan-in pattern splits a workload into parallel branches (fan-out), processes each branch concurrently, and combines the results (fan-in). How It Works Fan-out - A coordinator decomposes the input into independent chunks and dispatches each chunk to a separate model call. The decomposition can be static (split a document into pages) or dynamic (an LLM decides how to partition the work).https://ai-solutions.wiki/patterns/feedback-loop-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/feedback-loop-pattern/AI systems that ship without feedback mechanisms stagnate. The feedback loop pattern creates structured channels for capturing user reactions to AI outputs, then uses that signal to improve the system over time. Without this, you are guessing about quality. With it, you have a continuous stream of labeled data showing where the system succeeds and where it fails. Types of Feedback Explicit feedback - Users actively signal quality. Thumbs up/down buttons, star ratings, “was this helpful?https://ai-solutions.wiki/patterns/gdpr-compliant-ml-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/gdpr-compliant-ml-pipeline/Building an ML pipeline that satisfies GDPR requires embedding data protection controls at every stage, from data ingestion through model serving. This pattern describes the architectural components needed. Data Ingestion Layer The ingestion layer must enforce lawful basis verification before any personal data enters the pipeline. Implement a consent management service that checks whether valid consent exists for each data subject before their data is included in training datasets. For legitimate interest processing, verify that the balancing test has been documented.https://ai-solutions.wiki/patterns/gpu-pooling/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/gpu-pooling/GPUs are expensive and frequently underutilized. A team that owns dedicated GPU nodes for training uses them heavily during experiment sprints and leaves them idle between sprints. Meanwhile, another team waits weeks for GPU capacity. GPU pooling creates a shared infrastructure layer where GPU resources are allocated dynamically based on demand rather than statically assigned to teams. The Utilization Problem In a typical organization without pooling, each team provisions GPUs for peak demand.https://ai-solutions.wiki/patterns/graceful-degradation-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/graceful-degradation-ai/AI components fail. Model APIs go down, rate limits are exceeded, latency spikes occur, and output quality degrades. A well-designed system maintains useful functionality even when its AI components are impaired. Graceful degradation is not optional for production AI systems. Fallback Hierarchy Define multiple levels of functionality, from full AI-powered experience to basic non-AI operation. Level 1: Full AI - The system operates normally with the primary model providing full functionality.https://ai-solutions.wiki/patterns/guardrails-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/guardrails-pattern/Guardrails are validation and filtering layers placed before and after model calls to ensure AI outputs meet safety, quality, and compliance requirements. Input guardrails prevent harmful or malicious prompts from reaching the model. Output guardrails catch problematic content before it reaches the user. Together, they create a safety envelope around the model that reduces risk without requiring changes to the model itself. Input Guardrails Prompt injection detection - Identify attempts to override system instructions.https://ai-solutions.wiki/patterns/human-in-the-loop/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/human-in-the-loop/Human-in-the-loop (HITL) is not a single pattern but a spectrum of human involvement in AI-driven workflows. The right level of human involvement depends on the cost of errors, the maturity of the model, and the regulatory environment. Getting this balance wrong in either direction - too much human involvement (negating automation value) or too little (allowing unchecked errors) - is the most common failure mode in production AI systems. Review Queue Pattern The most common HITL implementation.https://ai-solutions.wiki/patterns/image-classification/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/image-classification/Image classification assigns labels to images. Modern approaches range from dedicated computer vision models (Amazon Rekognition) to multi-modal LLMs that can reason about image content. The choice depends on the classification task’s specificity, volume, and accuracy requirements. Classification Approaches Pre-built classification services - Amazon Rekognition, Google Vision, and similar services provide pre-trained classifiers for common categories: objects, scenes, faces, text, and content moderation. No training required. Best for standard classification tasks where the pre-built categories match your needs.https://ai-solutions.wiki/patterns/lakehouse-ai-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/lakehouse-ai-pattern/Traditional data architectures force a choice: store data in a warehouse for reliable analytics or in a data lake for flexible ML workloads. The lakehouse pattern eliminates this choice by adding warehouse-like reliability features (ACID transactions, schema enforcement, time travel) directly to data lake storage. Both analytics queries and ML training jobs read from the same data, in the same format, with the same governance controls. The Dual-System Problem Organizations that maintain separate warehouses and lakes suffer from data duplication, inconsistency, and operational overhead.https://ai-solutions.wiki/patterns/llmops-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/llmops-pipeline/MLOps pipelines built for traditional ML models do not address the unique operational requirements of large language models. LLMs are not retrained on every release. Their behavior is controlled primarily through prompts, retrieval configurations, and orchestration logic rather than model weights. An LLMOps pipeline manages the full lifecycle of these LLM-specific artifacts. Pipeline Stages Prompt development - Authors write and iterate on system prompts, few-shot examples, and output schemas in a version-controlled repository.https://ai-solutions.wiki/patterns/memory-pattern-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/memory-pattern-ai/LLMs are stateless by default. Each API call starts fresh with no memory of previous interactions. Conversational applications need memory to maintain context within a session and across sessions. Memory patterns range from simple conversation history management to sophisticated long-term knowledge stores that make the AI feel like it knows the user. Short-Term Memory: Within a Conversation Full conversation history - Append every user message and assistant response to the context window.https://ai-solutions.wiki/patterns/ml-feature-platform/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ml-feature-platform/Most ML teams compute features in ad-hoc scripts that differ between training notebooks and production serving code. The same feature gets reimplemented in Python for training and Java for serving, with subtle differences that cause training-serving skew. A feature platform centralizes feature definitions, computation, and serving so that the same feature logic is used everywhere. The Training-Serving Skew Problem Training-serving skew occurs when the features used during model training differ from those used during inference.https://ai-solutions.wiki/patterns/model-distillation/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/model-distillation/Model distillation uses a large, capable model (the teacher) to generate training data for a smaller, cheaper model (the student). The student learns to replicate the teacher’s behavior on a specific task at a fraction of the inference cost. This is the most effective cost optimization pattern for AI applications that have identified a stable, well-defined task. When to Distill Distillation is worth the effort when three conditions are met: you have a specific, well-defined task; you are processing enough volume that the cost difference between large and small models is significant; and the task’s requirements are stable enough that the distilled model will not need frequent retraining.https://ai-solutions.wiki/patterns/model-ensemble/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/model-ensemble/A single model has a single failure mode. An ensemble of models can compensate for individual weaknesses, improve accuracy, and provide built-in redundancy. But ensembles add complexity, cost, and latency that must be justified by measurable improvement. Voting Ensemble Multiple models process the same input independently, and the final output is determined by majority vote (for classification) or averaging (for scores and rankings). When to use it - When accuracy on critical decisions justifies the cost of multiple inference calls.https://ai-solutions.wiki/patterns/model-lineage/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/model-lineage/When a production model produces unexpected results, the first question is: what changed? Model lineage tracking provides the answer by maintaining a connected graph of every artifact, decision, and transformation in the model’s history, from raw data through training to deployment. What Lineage Captures Data lineage - Which datasets were used for training, validation, and testing. The specific version or snapshot of each dataset. Any preprocessing, filtering, or augmentation applied. The feature definitions and their computation logic.https://ai-solutions.wiki/patterns/model-tier-routing/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/model-tier-routing/Not every request needs your most expensive model. A simple classification task does not require the same compute as a complex multi-step analysis. Model tier routing evaluates incoming requests and directs them to the appropriate model tier - small, medium, or large - based on task complexity, quality requirements, and cost constraints. Organizations that implement tiered routing typically reduce their inference costs by 40-70% while maintaining output quality on the requests that matter most.https://ai-solutions.wiki/patterns/multi-model-routing/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/multi-model-routing/Not every request needs the most capable model. A simple classification task does not need the same model as a complex reasoning task, and paying for the most expensive model on every request is wasteful. Multi-model routing directs each request to the most appropriate model based on task characteristics. Complexity-Based Routing Route requests to models matched to the task’s complexity level. Implementation - A lightweight classifier analyzes the incoming request and assigns a complexity tier.https://ai-solutions.wiki/patterns/multi-provider-llm-failover/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/multi-provider-llm-failover/Depending on a single LLM provider creates a single point of failure. Provider outages, rate limit exhaustion, and regional incidents can take down your entire AI-powered application. Multi-provider failover maintains connections to multiple LLM providers and automatically routes traffic to a healthy provider when the primary becomes unavailable. Provider Health Checking Active health checks - Send lightweight probe requests to each provider on a regular interval (every 10-30 seconds). Measure response latency and verify response quality.https://ai-solutions.wiki/patterns/data-residency-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/data-residency-pattern/Organizations operating AI systems across multiple jurisdictions must ensure that data stays within its required legal boundaries while still enabling effective model training and inference. This pattern describes the architecture for multi-region data sovereignty. Pattern Overview Deploy region-specific data stores, training infrastructure, and inference endpoints. Data never leaves its jurisdiction of origin unless an explicit, documented transfer mechanism is in place. A global control plane coordinates model versions and configurations across regions without accessing the data itself.https://ai-solutions.wiki/patterns/multi-tenant-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/multi-tenant-ai/Multi-tenant AI systems serve multiple customers from shared infrastructure. This creates unique challenges: data must be isolated, resources must be fairly allocated, and the system must support per-tenant customization without per-tenant infrastructure. Data Isolation The most critical requirement. One tenant’s data must never leak to another tenant, even accidentally through model context, cache contamination, or logging. Prompt isolation - Every model call must include only the requesting tenant’s data. In RAG systems, vector search must filter by tenant ID to prevent cross-tenant retrieval.https://ai-solutions.wiki/patterns/orchestrator-worker/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/orchestrator-worker/Complex AI tasks rarely map cleanly to a single model call. The orchestrator-worker pattern uses one LLM as a coordinator that breaks down a complex request, delegates subtasks to specialized workers, and assembles their outputs into a coherent result. Architecture The orchestrator receives the original user request and produces a task plan: a list of subtasks with their dependencies. Each subtask is dispatched to a worker. Workers can be different models optimized for specific capabilities, the same model with different system prompts, or non-LLM tools like code interpreters and search engines.https://ai-solutions.wiki/patterns/pii-redaction-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/pii-redaction-pipeline/LLMs process free-text input that frequently contains personally identifiable information. Users paste emails, support tickets, medical notes, and financial documents into prompts without considering what sensitive data they include. A PII redaction pipeline intercepts this data before it reaches the model and scrubs sensitive information from responses before they reach the user. Why Redaction Matters Sending PII to a third-party model API creates compliance risk under GDPR, HIPAA, CCPA, and similar regulations.https://ai-solutions.wiki/patterns/plan-and-execute/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/plan-and-execute/The plan-and-execute pattern splits agent work into two distinct phases. A capable planner model analyzes the task, breaks it into concrete steps, and produces a structured plan. Then a cheaper executor model carries out each step independently. The planner may re-plan if execution results reveal the original plan was flawed. This separation reduces cost because the expensive model only runs once for planning, while the bulk of token-heavy execution work runs on a cheaper tier.https://ai-solutions.wiki/patterns/policy-as-code-ml/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/policy-as-code-ml/Governance policies for AI systems are often documented in spreadsheets, wiki pages, and slide decks that nobody enforces consistently. Policy as code converts these human-readable rules into executable checks that run automatically in the ML CI/CD pipeline. A model that violates a policy cannot be deployed because the pipeline blocks it, not because someone remembered to check. Why Policies Must Be Code Manual governance review does not scale. An organization deploying dozens of models across multiple teams cannot rely on a governance board to manually review every model promotion.https://ai-solutions.wiki/patterns/privacy-preserving-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/privacy-preserving-ai/Privacy-preserving AI encompasses a family of techniques that enable machine learning while minimizing exposure of sensitive data. These techniques are not mutually exclusive and are often combined to provide layered privacy protection. Federated Learning Federated learning trains a shared model across decentralized data sources without transferring raw data to a central location. Each participant trains the model locally on their data and sends only model updates (gradients or weights) to a central aggregation server.https://ai-solutions.wiki/patterns/progressive-delivery-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/progressive-delivery-ai/Deploying a new AI model is riskier than deploying a new application version. A model that passes evaluation tests can still fail on production traffic: edge cases the test set does not cover, latency differences under real load, or subtle quality degradation that metrics catch only at scale. Progressive delivery addresses this by gradually exposing new models to production traffic while monitoring AI-specific metrics and automatically rolling back when quality degrades.https://ai-solutions.wiki/patterns/prompt-injection-defense/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/prompt-injection-defense/Prompt injection is the most pervasive security risk in LLM-powered applications. An attacker crafts input that overrides the system prompt, causing the model to ignore its instructions and perform unintended actions. No single technique eliminates the risk entirely. Effective defense requires multiple independent layers, each reducing the attack surface so that a bypass at one layer is caught by another. Why Single-Layer Defense Fails A system that relies solely on input filtering will eventually encounter an encoding trick, unicode substitution, or multi-turn attack sequence that evades the filter.https://ai-solutions.wiki/patterns/prompt-template-management/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/prompt-template-management/Prompts are code. They define the behavior of your AI system as directly as any function or API endpoint. Yet most teams manage prompts in ad-hoc ways - hard-coded strings in application code, Google Docs shared among team members, or configuration files with no version history. This works for one prompt. It does not work for fifty. Prompts as Code Store prompt templates in version control alongside application code. Each prompt template is a file with a defined schema: the template text with variable placeholders, metadata (model target, temperature, max tokens), and a description of the prompt’s purpose and expected behavior.https://ai-solutions.wiki/patterns/rate-limiting-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/rate-limiting-ai/AI applications have unique rate limiting requirements. Model APIs impose their own limits (requests per minute, tokens per minute), costs scale with usage, and request processing times are orders of magnitude longer than traditional API calls. Effective rate limiting protects both your budget and your service quality. Token-Based Rate Limiting Traditional rate limiting counts requests. AI applications need to count tokens because a single request can consume vastly different amounts of capacity depending on input and output size.https://ai-solutions.wiki/patterns/react-pattern-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/react-pattern-ai/ReAct (Reasoning + Acting) is a prompting and agent architecture pattern where the model alternates between generating a reasoning trace and taking an action. Instead of producing a final answer in one shot, the agent thinks step by step, calls a tool, observes the result, reasons about the observation, and decides the next action. This loop continues until the agent has enough information to produce a final answer. The Core Loop A ReAct cycle has three phases that repeat:https://ai-solutions.wiki/patterns/stream-processing-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/stream-processing-ai/This page describes the pattern. For a full implementation guide covering pipeline architecture, late data handling, schema evolution, and event-driven inference, see Real-Time Data Pipelines for AI Workloads. The core problem this pattern solves: ML models need features that reflect current state, but computing features in batch introduces hours of staleness. For fraud detection, recommendation ranking, and dynamic pricing, a feature that is two hours old can be as misleading as no feature at all.https://ai-solutions.wiki/patterns/real-time-feature-serving/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/real-time-feature-serving/Online ML models that serve predictions in real time need feature values with single-digit millisecond latency. A fraud detection model evaluating a transaction cannot wait for a SQL query to compute the customer’s 30-day spending average. Real-time feature serving precomputes and caches feature values so they are available instantly at inference time. The Latency Budget A real-time inference request has a total latency budget, typically 50-200 milliseconds. This budget must cover feature retrieval, model inference, post-processing, and network overhead.https://ai-solutions.wiki/patterns/real-time-vs-batch/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/real-time-vs-batch/The choice between real-time and batch processing is not binary. Most AI systems need both, applied to different parts of the workload. The right split depends on latency requirements, cost sensitivity, and how the output is consumed. Decision Framework Real-time when - The user is waiting for the response. The value of the output decreases rapidly with delay (fraud detection, content moderation, conversational AI). The input volume is manageable within rate limits and cost budgets.https://ai-solutions.wiki/patterns/reflection-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/reflection-pattern/The reflection pattern has an LLM generate an initial response, then evaluate that response for errors, gaps, or quality issues, and produce an improved version. This self-critique loop can run once or multiple times, with each iteration refining the output. The pattern exploits the observation that LLMs are often better at identifying problems in existing text than avoiding those problems during initial generation. How It Works Step 1: Generate - The model produces an initial response to the prompt.https://ai-solutions.wiki/patterns/response-streaming/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/response-streaming/LLM responses take seconds to generate fully, but they are produced token by token. Streaming sends tokens to the user as they are generated rather than waiting for the complete response. This dramatically improves perceived latency - the user sees content appear within milliseconds instead of waiting seconds for a complete response. Why Streaming Matters Time-to-first-token (TTFT) is the metric that matters for user perception. A response that takes 5 seconds to generate fully but starts streaming after 200ms feels fast.https://ai-solutions.wiki/patterns/retrieval-routing/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/retrieval-routing/Not every question should hit the same knowledge source. A question about company policy should query the policy document store. A question about a customer’s order status should query the transactional database. A question about recent industry news should query a web search API. The retrieval routing pattern classifies incoming queries and directs each to the most appropriate knowledge source. Why Route A naive RAG implementation sends every query to a single vector store.https://ai-solutions.wiki/patterns/sandbox-testing-agents/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/sandbox-testing-agents/AI agents that use tools (databases, APIs, file systems, code execution) can cause real-world side effects during testing. A test that lets an agent call a production API, delete a database record, or execute arbitrary code is dangerous. The sandbox testing pattern provides isolated environments where agents can exercise their full tool-use capabilities without affecting production systems. The Problem Mocking every tool interaction is safe but incomplete. Mocked tools do not test whether the agent correctly handles real tool responses, real latency, real error formats, or real side effects.https://ai-solutions.wiki/patterns/self-healing-architecture/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/self-healing-architecture/Self-healing architecture uses AI to close the loop between failure detection and remediation. Traditional monitoring detects problems and alerts humans. Self-healing systems detect problems, diagnose root causes, select appropriate remediation actions, execute them, and verify recovery - all without human intervention. The AI component replaces the on-call engineer’s decision-making for known failure classes. The Self-Healing Loop Detect - Monitoring systems identify anomalies: elevated error rates, increased latency, resource exhaustion, failed health checks, unusual traffic patterns.https://ai-solutions.wiki/patterns/self-healing-model/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/self-healing-model/ML models degrade in production. The data distribution shifts, user behavior changes, and the relationship between features and targets evolves. A model that performed well at deployment time may be making poor predictions weeks later without anyone noticing. The self-healing model pattern automates the detection of degradation and triggers corrective action without waiting for a human to investigate. Degradation Signals Data drift - The statistical distribution of input features changes relative to the training distribution.https://ai-solutions.wiki/patterns/semantic-assertion/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/semantic-assertion/The semantic assertion pattern replaces exact string comparison in test assertions with semantic similarity checks. Instead of asserting that the AI output equals a specific string, you assert that it means the same thing as the expected output, even if the wording differs. The Problem AI systems express the same answer in many ways. “Paris is the capital of France,” “The capital of France is Paris,” and “France’s capital city is Paris” are all correct answers to the same question.https://ai-solutions.wiki/patterns/semantic-caching/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/semantic-caching/Traditional caching matches requests by exact key. For AI applications, this is almost useless because the same question phrased differently produces a cache miss every time. Semantic caching uses embedding similarity to match requests by meaning, dramatically improving cache hit rates. How Semantic Caching Works When a request arrives, it is converted to an embedding vector. This vector is compared against cached request embeddings. If a cached request is sufficiently similar (above a similarity threshold), the cached response is returned without making a model call.https://ai-solutions.wiki/patterns/sentiment-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/sentiment-pipeline/Sentiment analysis goes beyond positive/negative/neutral classification. Production systems need aspect-based sentiment (positive about the product, negative about shipping), intensity scoring (mildly annoyed vs. furious), and temporal tracking to detect shifts. Sentiment Dimensions Polarity - The basic positive/negative/neutral classification. Useful for high-level dashboards but too coarse for actionable insights. A product with 60% positive and 40% negative sentiment needs to know what is driving the negative to take action. Intensity - How strong is the sentiment?https://ai-solutions.wiki/patterns/shadow-deployment/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/shadow-deployment/Shadow deployment runs a new model alongside the production model, processing the same inputs, but only serving the production model’s outputs to users. The shadow model’s outputs are logged for comparison. This lets you evaluate a new model on real production traffic without any risk to users. When to Use Shadow Deployment Shadow deployment is the right choice when you need to validate a new model on real data that cannot be adequately represented by test datasets.https://ai-solutions.wiki/patterns/statistical-assertion/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/statistical-assertion/The statistical assertion pattern replaces exact-match test assertions with aggregate success rate checks across multiple runs. Instead of asserting that a single AI output matches an expected value, you run the test N times and assert that the success rate exceeds a threshold with statistical confidence. The Problem AI systems produce different outputs for the same input. A test that asserts response == "Paris" will pass when the model says “Paris” and fail when it says “The capital of France is Paris.https://ai-solutions.wiki/patterns/structured-output/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/structured-output/When LLMs feed downstream systems rather than human readers, the output must be structured and parseable. A pipeline that expects a JSON object with specific fields cannot handle a conversational response that wraps the data in markdown and adds explanatory text. Structured output patterns ensure the model produces exactly the format your system needs, every time. Approaches to Structured Output Prompt-based JSON - Include explicit instructions in the prompt: “Respond with a JSON object containing the fields: category (string), confidence (float between 0 and 1), and reasoning (string).https://ai-solutions.wiki/patterns/summarization-chain/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/summarization-chain/Summarizing a document that fits within a model’s context window is straightforward. Summarizing a 200-page report, a day’s worth of Slack messages, or a multi-hour meeting transcript requires a chain of summarization steps because the source material exceeds what a single model call can process. Map-Reduce Summarization Split the document into chunks, summarize each chunk independently (map), then summarize the chunk summaries into a final summary (reduce). Map phase - Split the document into chunks that fit within the model’s context window.https://ai-solutions.wiki/patterns/token-optimization/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/token-optimization/Token usage drives LLM costs directly. Every unnecessary token in your prompt or response is money spent on content that does not improve the output. Token optimization is not about being cheap - it is about being precise with what you send to the model and what you ask it to produce. Input Token Optimization Reducing input tokens means sending the model less text while preserving the information it needs to produce good output.https://ai-solutions.wiki/patterns/tool-use-pattern/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/tool-use-pattern/Tool use (also called function calling) lets an LLM invoke external functions, APIs, and services during a conversation. Instead of the model guessing at information or admitting it cannot perform an action, it calls a tool: look up a database record, execute code, search the web, send an email, or query an internal system. The model decides which tool to call, what parameters to pass, and how to incorporate the result into its response.https://ai-solutions.wiki/patterns/translation-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/translation-pipeline/AI translation has reached the point where it produces usable first drafts for most language pairs and content types. But a production translation pipeline requires more than a single model call - it needs terminology consistency, format preservation, quality assurance, and efficient orchestration across multiple target languages. Pipeline Architecture A production translation pipeline has four stages: pre-processing, translation, post-processing, and quality assurance. Pre-processing - Extract translatable text from the source format while preserving structure markers.https://ai-solutions.wiki/patterns/vcr-pattern-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/vcr-pattern-ai/The VCR (Video Cassette Recorder) pattern records real HTTP interactions with external APIs and replays them in subsequent test runs. For AI API testing, this means calling the real LLM or embedding API once, saving the response to a cassette file, and replaying that exact response in every future test run. Tests become deterministic, fast, and free of API costs. How It Works Record mode. The first time a test runs, HTTP requests pass through to the real API.https://ai-solutions.wiki/patterns/vector-index-management/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/vector-index-management/Vector search systems underpin RAG applications, semantic search, and recommendation engines. The vector index that powers these systems is not a static artifact. Documents are added, updated, and deleted. Embedding models are upgraded. Index parameters need tuning as the corpus grows. Vector index management treats the index as a production artifact with its own lifecycle, versioning, and operational practices. Index Building Pipeline Document preprocessing - Chunk source documents into segments appropriate for the embedding model’s context window.https://ai-solutions.wiki/patterns/vector-search-optimization/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/vector-search-optimization/Vector search is the retrieval backbone of RAG systems. Getting it right determines whether the AI system finds relevant context or generates responses from irrelevant or missing information. Optimization targets three dimensions: relevance (finding the right content), performance (finding it fast), and cost (finding it efficiently). Index Optimization The vector index structure determines the speed-accuracy tradeoff for search operations. HNSW tuning - HNSW indexes have two key parameters: M (connections per node) and efConstruction (construction-time search breadth).https://ai-solutions.wiki/patterns/video-analysis-pipeline/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/video-analysis-pipeline/Video analysis combines multiple AI capabilities - visual recognition, audio transcription, text detection, and temporal reasoning - into a pipeline that must process hours of content efficiently. The challenge is not any single analysis step but orchestrating them together with aligned timestamps and manageable costs. Frame Extraction Strategy Video is a sequence of frames, but analyzing every frame is wasteful and expensive. The extraction strategy determines the cost-quality tradeoff. Fixed-rate extraction - Extract frames at a constant rate (1 per second, 1 per 5 seconds).https://ai-solutions.wiki/patterns/zero-trust-ai/Sat, 28 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/zero-trust-ai/Traditional perimeter-based security assumes that internal services are trustworthy. In AI systems, this assumption is dangerous. A compromised inference service can exfiltrate model weights (valuable intellectual property). A compromised data pipeline can poison training data. A prompt injection can manipulate model behaviour from outside the perimeter. Zero trust for AI applies “never trust, always verify” to every layer of the ML stack. Threat Model for AI Systems Before applying zero trust, understand what you are protecting:https://ai-solutions.wiki/patterns/caching-for-ai/Thu, 26 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/caching-for-ai/Model inference is expensive and slow compared to returning a cached result. In AI applications, the decision of what to cache and how to cache it has a larger impact on cost and performance than almost any other architectural choice. This article covers the four main caching patterns for production AI systems. Why Caching Matters More for AI A conventional API call might cost fractions of a cent and complete in under 100ms.https://ai-solutions.wiki/patterns/retry-and-backoff/Thu, 26 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/retry-and-backoff/Every distributed system needs retry logic. AI services need it more than most, and they need it differently. A conventional API rate limit is measured in requests per second. An AI service rate limit is measured in tokens per minute, which means a burst of short requests and a burst of long requests hit the limit at completely different rates. Model inference also takes longer than a database query, which changes the math on timeout and retry budget design.https://ai-solutions.wiki/patterns/tiered-analysis/Thu, 26 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/tiered-analysis/The tiered analysis pattern addresses a fundamental cost problem in AI pipelines: expensive AI operations (large language model calls, detailed vision analysis) are orders of magnitude more costly than cheap operations (basic classification, label detection). Applying maximum-depth analysis to every input is almost never necessary - and often prohibitively expensive. The pattern: apply cheap analysis first, score results, then apply expensive analysis only to candidates that pass a threshold. The Problem Consider processing a three-hour video to find the best five-second clips.https://ai-solutions.wiki/patterns/blue-green-deployment/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/blue-green-deployment/Blue-green deployment is a release technique that reduces downtime and deployment risk by running two identical production environments - one live (blue), one idle (green) - and switching traffic between them when a new version is ready. For AI services, blue-green deployment solves a specific problem: model updates that change output behaviour in ways that unit tests cannot fully predict. How Blue-Green Deployment Works At any point in time, one environment serves all production traffic.https://ai-solutions.wiki/patterns/canary-deployment/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/canary-deployment/A canary deployment releases a new version to a small subset of traffic before expanding to the full user base. The name comes from the historical practice of taking a canary into coal mines: the bird would alert miners to dangerous gases before concentrations reached levels harmful to humans. In software, the “canary” is a small fraction of production traffic exposed to the new version first, alerting the team to problems before all users are affected.https://ai-solutions.wiki/patterns/circuit-breaker-ai/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/circuit-breaker-ai/Model APIs fail. They time out under high load, return rate limit errors when traffic spikes, and occasionally return malformed responses that cannot be parsed. A production AI service that propagates these failures directly to users provides a worse experience than gracefully degrading to a simpler alternative. The circuit breaker pattern protects your system from cascade failure when upstream AI services are unhealthy. How Circuit Breakers Work A circuit breaker wraps calls to an external service and tracks failure rate over a sliding time window.https://ai-solutions.wiki/patterns/event-sourcing-ai/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/event-sourcing-ai/Event Sourcing treats every state change as an immutable event appended to a log. Instead of storing the current state of a record, you store the full sequence of events that produced that state. The current state is derived by replaying the log. For AI systems, this pattern solves several problems that are hard to address with mutable state stores: audit trails, pipeline replay, debugging data quality issues, and reconstructing model inputs retrospectively.https://ai-solutions.wiki/patterns/feature-flags-ai/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/feature-flags-ai/Model deployments are not like code deployments. A code change is either correct or incorrect - tests can verify it. A model change produces outputs that are statistically better or worse, and that difference often only becomes visible under real production traffic with real user queries. Feature flags give you control over which model handles which traffic, enabling safe rollout, A/B comparison, and instant rollback without redeployment. What Feature Flags Enable for AI Canary deployment - Route 5% of traffic to the new model, monitor quality metrics and error rates, then increase the percentage gradually.https://ai-solutions.wiki/patterns/microservices-for-ai/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/microservices-for-ai/An AI system built as a monolith ships fast initially but becomes brittle under load, expensive to scale selectively, and risky to update. Decomposing AI systems into independent services applies the same reasoning that drove microservices adoption in backend engineering: isolate failure domains, scale hot components independently, and deploy without coordinating every team. Service Decomposition for AI Pipelines A typical AI pipeline can be decomposed along its functional seams: Ingestion Service - Accepts raw documents, events, or data feeds.https://ai-solutions.wiki/patterns/model-versioning/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/model-versioning/A model version is a specific combination of: model weights, prompt template, configuration parameters, and evaluation metrics - captured at a point in time. Without versioning, you cannot reproduce a previous model’s behaviour, cannot attribute a quality change to a specific deployment, and cannot roll back to a known-good state. For production AI systems, model versioning is the mechanism that makes deployments auditable and reversible. What Constitutes a “Model Version” A model version in a production AI system is not just the model weights.https://ai-solutions.wiki/patterns/observability-ai/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/observability-ai/Observability is the ability to understand the internal state of a system from its external outputs. For traditional software, three categories of output provide this understanding: logs (discrete events), metrics (numeric measurements over time), and traces (the path a request takes through a distributed system). AI systems generate all three but require additional instrumentation to capture the information that matters: token usage, response quality, cost per request, and model version attribution.https://ai-solutions.wiki/patterns/strangler-fig-ai/Wed, 25 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/strangler-fig-ai/The Strangler Fig pattern was named and described by Martin Fowler in 2004, drawing on the metaphor of a strangler fig plant that grows around a host tree, gradually replacing it. The pattern describes a migration strategy: rather than replacing a legacy system all at once (a “big bang” migration), you incrementally route functionality through a new system while keeping the legacy system running. Over time, the new system handles more and more traffic until the legacy system can be retired.https://ai-solutions.wiki/patterns/agentic-workflows/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/agentic-workflows/Agentic AI workflows go beyond single model calls. An agent can use tools, take actions, and decide what to do next based on results. But “agentic” covers a wide range of architectural patterns with very different complexity profiles. Choosing the right pattern for the problem avoids over-engineering simple workflows and under-engineering complex ones. Chain Pattern The simplest agentic pattern. Step A produces output that becomes input to Step B, which feeds Step C.https://ai-solutions.wiki/patterns/cost-optimization/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/cost-optimization/AI inference costs in production are real and can be significant if not managed. A production system processing thousands of calls per day at premium model rates can easily accumulate 10,000-50,000 EUR per month in API costs. Cost optimization does not mean accepting lower quality - it means applying the right capability to each task at the right price. Tiered Model Selection Not all tasks require the same capability. Claude’s model family illustrates the spectrum:https://ai-solutions.wiki/patterns/ai-governance/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/ai-governance/AI governance is the set of processes, documentation, and controls that ensure AI systems in an organization are accountable, auditable, and compliant. As the EU AI Act enters into force, governance is shifting from a good practice to a legal requirement for many AI applications. Building governance patterns from the start is significantly less expensive than retrofitting them. Model Cards A model card is a structured document that describes an AI model’s purpose, training data, performance characteristics, known limitations, and intended use boundaries.https://ai-solutions.wiki/patterns/context-window-management/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/context-window-management/Every language model has a context window - the maximum amount of text it can process in a single call. Claude 3.5 Sonnet supports 200,000 tokens; GPT-4o supports 128,000. These are large, but real-world applications regularly exceed them: long documents, extended conversations, large codebases, multi-document research. Context window management is the set of patterns for handling content that does not fit. Summarization Pattern Compress past content to make room for new content.https://ai-solutions.wiki/patterns/data-pipeline-patterns/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/data-pipeline-patterns/AI systems are only as good as the data that feeds them. Most AI project failures trace back to data problems - not model problems. These patterns address the most common data pipeline challenges in production AI workloads. Pattern 1 - Separate Raw, Processed, and Feature Layers Structure your data lake with three distinct layers: Raw layer - Immutable, append-only storage of data exactly as it arrived from source systems. Never modify raw data.https://ai-solutions.wiki/patterns/evidence-bundling/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/evidence-bundling/Any AI system that produces recommendations affecting people’s access to services, money, or rights needs to be able to show its work. Evidence bundling is the design pattern that makes this possible: instead of producing a recommendation with an opaque score, the system collects, organizes, and presents the source material that supports the recommendation. Why Evidence Bundling Matters AI recommendations without evidence have two problems. First, human reviewers cannot meaningfully evaluate them - rubber-stamping is the only available action when there is nothing to evaluate.https://ai-solutions.wiki/patterns/prompt-engineering-patterns/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/prompt-engineering-patterns/Prompt engineering is the practice of designing inputs to language models to reliably produce useful outputs. In enterprise applications, prompts are not one-off experiments - they are code. They need to be versioned, tested, and maintained. These patterns reflect what works at scale. Pattern 1 - Structured Output with JSON Schema For any application that processes LLM output programmatically, request JSON output with an explicit schema. This is more reliable than parsing natural language responses and fails more gracefully when the model deviates.https://ai-solutions.wiki/patterns/rag-implementation/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/rag-implementation/Retrieval Augmented Generation (RAG) is the most commonly deployed AI pattern in enterprise settings. It solves a fundamental limitation of LLMs: they do not know about your private data, your recent documents, or your organization’s specific knowledge. RAG provides that knowledge at query time by retrieving relevant documents and passing them to the model along with the question. Building a RAG system that works in demos is straightforward. Building one that works reliably in production requires attention to a set of patterns that are not obvious at the outset.https://ai-solutions.wiki/patterns/scoring-and-prioritization/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/scoring-and-prioritization/Prioritization is one of the highest-value applications of AI in operational contexts. When a queue contains more items than can be processed immediately, the order of processing matters. AI scoring allows that order to be determined by a consistent, auditable formula rather than whoever arrived first or whoever called the loudest. The Core Problem with Queues First-in, first-out (FIFO) is not a prioritization strategy - it is an abdication of one.https://ai-solutions.wiki/patterns/intake-to-action/Tue, 24 Mar 2026 00:00:00 +0000https://ai-solutions.wiki/patterns/intake-to-action/The intake-to-action pattern appears wherever an organization receives unstructured information from external parties and needs to act on it. Claims arrive as document packets. Benefit applications arrive as scanned forms. Legal referrals arrive as narrative descriptions. In every case, the same fundamental transformation is needed: convert the unstructured input into a structured record, identify what is missing or flagged, and determine what should happen next. The Core Transformation The pattern has three stages: