evaluation

Learn how to systematically evaluate Retrieval-Augmented Generation systems in production using RAGAS, TruLens, and Phoenix — covering golden sets, retrieval drift, embedding drift, and cost-aware eval scheduling.

A rigorous look at how to measure agent performance — trajectory-level vs final-answer evals, canonical multi-step benchmarks (SWE-bench, WebArena, OSWorld, GAIA), LLM-as-judge pitfalls, and why your eval is your spec.

A rigorous guide to measuring RAG quality using RAGAS metrics — faithfulness, answer relevancy, context precision, and context recall — plus how to build a golden dataset and recognize where automated metrics fall short.

A field guide to the LLM benchmarks practitioners cite in 2026 — what each one measures, where it's saturated, where contamination risk is high, and why benchmark gains rarely transfer to your task without your own eval.

A practical decision framework for picking an LLM in 2026 — define the task, build an offline eval, measure quality + latency + cost on real candidates, and avoid the classic trap of optimizing for benchmarks instead of your task.

A capability-by-capability tour of frontier LLMs in 2026 — which models are strong at reasoning, code, math, long-context, multilingual, multimodal, and tool use, with hedged comparisons instead of point-estimate benchmark wars.

Dive into LangSmith, the developer platform for building and evaluating robust Large Language Model (LLM) applications. Learn how to trace execution paths, debug complex chains, and rigorously evaluate your LLM's performance to ensure reliability and quality.