Stop using guesswork to find out how your AI applications are performing. Evals for AI Engineers gives you a practical framework for systematically testing, measuring, and improving AI applications, especially those built with LLMs. Written by Shreya Shankar and Hamel Husain, AI engineers with extensive experience across 35+ AI products and cutting-edge research, this book focuses on the techniques teams actually need to build reliable AI systems in production. Ideal for software engineers, technical product managers, and technical leads, this hands-on guide shows you how to build an evaluation process that improves alongside your product.
Find and prioritize failures through systematic error analysis and representative test sets Build trustworthy evaluations using code based checks, LLM judges, human feedback, and synthetic data Improve production systems over time by monitoring regressions and optimizing quality, latency, and cost.
Transform your engineering curriculum and elevate code optimization protocols with Evals for AI Engineers, a premier digital textbook designed to shift students from baseline model prompts into advanced, data-driven evaluation mechanics. Engineered specifically for computer science faculty, machine learning infrastructure chairs, and interactive studio directors, this comprehensive reference provides the ultimate engineering blueprint for constructing rigid evaluation pipelines around automated applications. Whether you are aiming to deploy automated LLM-as-a-judge patterns, configure continuous integration monitoring for regression sweeps, calculate custom semantic distance metrics, or track cost-to-latency boundaries in edge-case arrays, this expert asset provides the ultimate architectural layout framework.
Packed with rigorous prompt-testing models, data validation matrices, and empirical algorithmic evaluation metrics, this digital textbook ensures technical cohorts develop sharp statistical reasoning and robust optimization habits. Deployed seamlessly through flexible cloud licensing paths, this zero-footprint courseware synchronizes reading progress, custom annotation histories, and code reference logs across all desktop and mobile devices, optimizing academic lab budgets while completely eliminating physical freight and warehouse overhead.
Please Note: This is a digital eBook. You will receive instant access after purchase. No physical book will be shipped.
