"OpenAI Evals Cookbook: Designing Benchmarks for Product‑Grade LLM Features"
Large language model features rarely fail in obvious ways: they drift, regress, overfit, and break at the edges where real users live. This book is written for experienced practitioners—ML engineers, applied researchers, platform teams, and technical product owners—who need evaluation systems strong enough to support production decisions. Rather than treating evals as a side project or leaderboard exercise, it frames them as the operational discipline that makes reliable LLM products possible.
Across the book, readers learn how to turn vague feature goals into measurable contracts, map meaningful failure modes, design resilient datasets, build graders and scoring logic, set thresholds for release decisions, and interpret results under non-determinism. It also covers slice analysis, root-cause diagnosis, evaluation-driven iteration, hosted OpenAI eval workflows, structured outputs, and advanced patterns for agent traces, cross-model benchmarking, and quality-cost model selection. The emphasis is on benchmarks that remain trustworthy as prompts, models, tools, and workflows evolve.
The treatment is practical, current, and technically rigorous. Familiarity with LLM application development, API-based model integration, and experimentation workflows is assumed. Organized as a progressive cookbook for advanced readers, the book combines architectural framing, design guidance, operational trade-offs, and modern platform-aware practice—helping teams build eval programs that can govern real releases, not
