Evals for AI Engineers

Preface

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Large language models have moved from research curiosity to production reality with remarkable speed. Organizations now routinely embed LLMs in customer service systems, content generation pipelines, decision support tools, and information extraction workflows. Yet this rapid adoption has outpaced our ability to systematically evaluate whether these systems actually work.

This book addresses that gap.

Unlike traditional software with deterministic outputs, LLM pipelines produce responses that are often subjective, context-dependent, and multifaceted. A response might be factually accurate yet inappropriate for the context. It might sound persuasive while conveying incorrect information. It might also address most but not all parts of the user’s question. These ambiguities make evaluation fundamentally different from conventional software testing or even traditional machine learning validation.

The challenge we tackle is straightforward to state but difficult to solve: How do you assess whether an LLM pipeline is performing adequately? How do you diagnose where it’s failing? And how do you systematically ...