AI Evals Engineering: Building Production-Ready Evaluation Systems for LLMs, RAG, AI Agents, and Modern AI Platforms
Building AI applications is easy. Building Production AI systems that remain accurate, reliable, scalable, and trustworthy in real-world environments is an entirely different challenge.
If you're developing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) applications, or autonomous AI agents, you need far more than prompts and benchmarks. You need a disciplined approach to AI Evaluation, AI Quality Engineering, and continuous validation that ensures your intelligent systems perform reliably from development through production.
AI Evals Engineering provides that complete engineering framework.
Rather than treating LLM Evaluation, RAG Evaluation, and AI Testing as isolated activities, this book shows you how to build a production-ready evaluation platform that integrates AI Benchmarking, AI Metrics, AI Observability, AI Governance, continuous regression testing, and operational monitoring into a unified engineering architecture. You'll learn how modern LLMOps practices transform evaluation into an automated, continuous process that strengthens AI systems throughout their entire lifecycle.
Inside you'll learn how to:Design enterprise-grade AI Evaluation architectures
Build high-quality benchmark datasets and reusable evaluation libraries
Master LLM Evaluation for correctness, reasoning, consistency, and hallucination detection
Engineer reliable RAG Evaluation pipelines with groundedness and citation validation
Implement AI Agent Engineering practices for evaluating planning, memory, tool usage, and task completion
Integrate automated AI Testing into CI/CD and modern LLMOps workflows
Measure performance using production-ready AI Metrics and AI Benchmarking strategies
Deploy AI Observability with MLflow, Phoenix, and OpenTelemetry for continuous production monitoring
Build scalable Enterprise AI evaluation platforms with governance, security, and compliance
Continuously improve AI quality through automated regression testing, monitoring, and operational feedback
Whether you're an AI Engineer, Machine Learning Engineer, Platform Engineer, Software Architect, LLMOps Engineer, or Engineering Leader, this book provides the practical blueprint for designing, deploying, evaluating, and continuously improving intelligent systems at enterprise scale.
Stop guessing whether your AI works. Master AI Evals Engineering and build Production AI systems that are measurable, reliable, observable, and ready for the enterprise.
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