8 docs tagged with "ai-evaluation"

Why evaluation is the hardest unsolved problem in AI engineering - and how to approach it systematically.

Learn how to construct, annotate, validate, and maintain golden datasets that serve as the ground truth foundation for all AI system evaluation - covering annotation guidelines, inter-annotator agreement, adversarial generation, dataset versioning, and drift detection.

Design and implement a full CI/CD pipeline for AI systems - covering PR-level linting, merge-level regression, pre-deployment evaluation gates, production monitoring with statistical process control, anomaly detection, automated rollback, and observability tracing from query to feedback.

Build calibrated, bias-corrected LLM judges that approximate human judgment at scale - pointwise scoring, pairwise comparison, bias mitigation, and ensemble techniques.

Design an evaluation strategy that bridges static datasets and production signals - A/B testing, shadow evaluation, implicit signals, and the evaluation flywheel.

Master the full evaluation stack for Retrieval-Augmented Generation systems - covering RAGAS metrics, hallucination type classification, citation accuracy, retrieval precision/recall/nDCG, and production-grade benchmarking with complete Python implementations.

Build a production-grade regression testing system for LLM prompts - covering test case design, LLM-as-judge pass/fail evaluation, flaky test detection, caching, differential testing, and CI gates that block regressions before they reach users.

Understanding the fundamental gap between software testing and AI evaluation - non-determinism, no oracle, emergent failures, and how to build a multi-layered evaluation strategy.