ai-engineering

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 practical deep-dive into fan-out, pipeline staging, judge panels, adversarial verification, and loop-until-dry — plus an honest accounting of when spawning subagents actually earns its token cost and when it just burns money.

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.