"AI Evaluation with Braintrust: Building Reliable LLM Products with Systematic Evals, Logging, and Experimentation"
Reliable LLM products are not built by intuition, prompt tinkering, or isolated demos—they are built through disciplined evaluation. This book is for experienced AI engineers, platform teams, and technical leads who need a rigorous way to measure, compare, and improve model behavior in real systems. Using Braintrust as the operational foundation, it shows how to turn evaluation into a repeatable engineering practice rather than an after-the-fact quality check.
Across the book, readers learn how to design high-signal datasets, define product-centered evals, build trustworthy scorers, and use experiments as immutable evidence for fair comparison. It covers LLM-as-a-judge, custom code-based scoring, parameter matrices, reproducibility, and the interpretation of score distributions beyond simplistic averages. The book also connects offline evaluation with production logging, tracing, regression detection, CI/CD gates, and remote execution, creating a full feedback loop from live failures to better benchmarks.
Rather than offering generic benchmarking advice, this guide focuses on the concrete mechanics of shipping and operating reliable LLM applications. Readers should already be comfortable with modern software engineering, APIs, and core LLM concepts. In return, they will gain a practical methodology for building evaluation systems that scale with product complexity, organizational demands, and the realities of production AI.
