Building Reliable AI Systems

Build AI systems people can actually rely on

Building Reliable AI Systems focuses on the part of AI work that matters most once a prototype leaves the lab: making it behave consistently in production. Rush Shahani frames reliability as more than accuracy alone. The book looks at grounded outputs, safe agents, and dependable operations, then shows how those layers fit together in real products.

The result is a practical, engineering-minded guide for teams working with large language models, retrieval systems, tools, and autonomous workflows. Rather than treating AI as a magic box, the book breaks reliability into decisions you can design, test, and monitor.

What the book explores

• Prompting and model settings for more stable, predictable output
• Retrieval-augmented generation for grounding answers in real data
• Embeddings, vector search, and hybrid retrieval strategies
• Fine-tuning and when it fits better than prompting or RAG
• AI agents, tool use, MCP, and multi-agent coordination
• Evaluation, monitoring, and operational practices for production systems

Why it stands out

Many AI books stop at getting a model to respond. This one keeps going, asking what happens when the system must work again tomorrow, under load, with real users, imperfect data, and changing conditions. That makes it especially relevant for engineers and data scientists building customer-facing or business-critical applications.

Who it suits

If you are developing production AI systems, experimenting with agent workflows, or trying to reduce hallucinations and brittle behavior, this book offers a structured path forward. It is aimed at readers who already want to build, not just browse theory.