Patterns
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Zero Trust for AI Model Serving
Applying zero trust architecture to AI systems: securing inference endpoints, model artifact access, training …Video Analysis Pipeline Patterns
Architecture patterns for AI-powered video analysis. Frame extraction, multi-modal analysis, temporal …Vector Search Optimization Patterns
Improving vector search quality and performance. Index tuning, hybrid search, re-ranking, and query …Vector Index Management
Lifecycle management for vector embeddings: index building, versioning, refresh strategies, quality …VCR Pattern for AI API Testing
Record-and-replay pattern for AI API testing: capture real model responses once, replay them in CI for …Translation Pipeline Patterns
Building production translation pipelines with AI. Terminology management, quality assurance, and …Tool Use Pattern - Function Calling for AI Agents
Enabling LLMs to invoke external tools and APIs through function calling, extending model capabilities beyond …Token Optimization Patterns for LLM Applications
Strategies for reducing token usage without sacrificing output quality. Prompt compression, context pruning, …Summarization Chain Patterns
Multi-step summarization strategies for long documents. Map-reduce, hierarchical, and iterative refinement …Structured Output - Enforcing JSON and Schema Compliance from LLMs
Techniques for getting reliable, machine-parseable structured output from LLMs: JSON mode, schema enforcement, …Statistical Assertion Pattern
A testing pattern for non-deterministic AI outputs: run N times, assert success rate exceeds threshold, use …Shadow Deployment Pattern for AI Models
Running new AI models in parallel with production models to compare outputs without affecting users. …Sentiment Analysis Pipeline Patterns
Building production sentiment analysis pipelines. Multi-dimensional sentiment, aspect-based analysis, and …Semantic Caching for AI Applications
Caching AI model responses based on semantic similarity rather than exact match. Implementation patterns, …Semantic Assertion Pattern
Asserting AI output correctness via semantic similarity rather than exact string match: embedding-based …Self-Healing Model Pattern
Automated drift detection, performance monitoring, and retraining triggers that keep ML models healthy in …Self-Healing Architecture - AI-Powered Automated Recovery
Using AI to detect, diagnose, and automatically remediate infrastructure and application failures without …Sandbox Testing Pattern for AI Agents
Sandboxed execution environments for testing AI agents with real tool access without production side effects: …Retrieval Routing Pattern
Smart routing between multiple knowledge sources based on query intent, selecting the optimal retrieval …Response Streaming Patterns for AI Applications
Implementing streaming responses from LLMs for improved perceived latency. Server-sent events, chunked …Reflection Pattern - Self-Critique and Iterative Refinement for LLMs
Using self-reflection loops where an LLM evaluates and improves its own output, catching errors and improving …Real-Time vs Batch AI Processing - Choosing the Right Pattern
Decision framework for choosing between real-time and batch AI processing. Latency requirements, cost …Real-Time Feature Serving
Sub-millisecond feature serving for online inference: architecture, caching strategies, precomputation …Real-Time Feature Computation Pattern
The architectural pattern for computing ML features from event streams: windowed aggregations, stream-table …
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