A/B Testing for AI Systems
How to design and run A/B tests for AI models and features, covering experiment design, traffic splitting, metrics selection, and …
Evaluation
AI Red Team
A dedicated adversarial testing team that probes AI systems for vulnerabilities, biases, safety failures, and misuse potential before and …
AI Spark: Smart Vendor Evaluation Scoring
Use AI to standardize vendor evaluation by scoring proposals against weighted criteria and generating comparison reports.
Architecture Decision Records and Evaluation Methods
Using ADRs and architecture evaluation methods like ATAM to document and assess architecture decisions in AI/ML systems.
DeepEval vs Promptfoo for LLM Evaluation in CI
Comparing DeepEval and Promptfoo for automated LLM evaluation: metrics, CI integration, configuration, pricing, and when to choose each.
Embedding Model Comparison and Selection Guide
How to choose embedding models for semantic search, RAG, and similarity tasks, comparing popular models across quality, speed, cost, and …
Evaluating RAG System Quality
How to measure and improve both retrieval quality and generation quality in RAG systems, with practical metrics and evaluation frameworks.
Evaluator-Optimizer Pattern
Automated evaluation loops where one model generates output and another evaluates it, driving iterative improvement until quality thresholds …
Framework for Evaluating and Selecting AI Vendors
A structured approach to evaluating AI vendors covering technical capabilities, data handling, compliance, pricing, and long-term viability.
Full-Stack Observability for AI Systems
How to implement comprehensive observability for AI applications covering traces, evaluations, metrics, and alerting across the entire …
Golden Dataset
What a golden dataset is, how it serves as a curated evaluation benchmark for measuring AI model quality, and best practices for building …
Ground Truth
What ground truth is in machine learning, how verified correct labels are obtained, and why ground truth quality directly bounds model …
LLM Evaluation Methods - Measuring Language Model Quality
A comprehensive guide to evaluating large language models, covering automated metrics (BLEU, ROUGE, BERTScore), LLM-as-judge, human …
LLMOps Pipeline
Production pipeline design for LLM-specific operations: prompt management, evaluation, deployment, monitoring, and cost tracking across the …
RAG Evaluation
Methods and metrics for measuring the quality of Retrieval Augmented Generation systems, covering retrieval accuracy, generation …
Red Teaming
What red teaming is in AI, how adversarial testing discovers vulnerabilities and failure modes before deployment, and best practices for …
Red Teaming and Adversarial Testing for AI Systems
How to plan and execute red team exercises that systematically probe AI systems for vulnerabilities, biases, and failure modes before …
Testing and Evaluating AI Agent Performance
Frameworks for evaluating AI agents that plan, use tools, and take actions, covering correctness, reliability, safety, and cost efficiency.
Testing LLM Applications
LLM-specific testing strategies: prompt template testing, structured output validation, guardrail verification, token limit testing, model …
Testing RAG Systems
How to test Retrieval-Augmented Generation systems: unit testing chunking, integration testing retrieval quality, testing citation accuracy, …
Testing AI Systems - Unit Tests to Production Monitoring
A practical testing strategy for AI systems: property-based testing, integration testing with mocked models, evaluation frameworks, and …
Langfuse - LLM Observability and Tracing
Using Langfuse to trace LLM calls, evaluate outputs, and monitor AI application quality in production.
Model Cards - AI Transparency Documentation
What model cards document, why they matter for AI governance, and how to create one.
The Use Case Scoring Framework - From 57 Ideas to 3 Prototypes
A structured WSJF-inspired scoring methodology to cut through workshop noise and identify the AI use cases worth building first.