Building Reliable AI Systems: Applications and Agents You Can Trust

This book provides a comprehensive framework for building reliable AI systems, focusing on large language models (LLMs) in real-world applications. It introduces a three-layer AI reliability framework: reliable outputs, reliable agents, and reliable operations, guiding developers through each stage.

The book covers prompt engineering techniques to reduce hallucinations, grounding outputs with Retrieval-Augmented Generation (RAG), and building trustworthy AI agents. It also addresses operational aspects like monitoring, evaluation, and deployment to ensure long-term reliability.

Through practical examples and projects, such as a weather assistant with function calls, readers learn to apply these concepts in real scenarios. The book emphasizes moving beyond benchmark performance to achieve dependable AI in production.

Targeted at developers, data scientists, and AI practitioners, this book bridges the gap between experimental AI and production-ready systems. It offers actionable insights and best practices for creating AI applications that users can trust.