The gap between an AI demo and a production system is enormous. Your RAG pipeline that works on a test set might return garbage in production. Your agent that handles 99% of cases can cause catastrophic damage on the 1% that matters.
This book is about closing that gap. It gives you a hands-on guide to building reliable, cost-effective, and safe AI systems. You will learn a capability and reliability framework that ensures your model's power is amplified while its risks are contained.
Inside, you'll discover:
• Choosing the right model for your specific task
• Systematic prompt engineering as software engineering
• Building reliable retrieval-augmented generation systems
• Structured outputs and function calling for reliability
• Designing agents that don't spiral out of control
• Evaluation strategies for production AI
• Cost optimization, latency management, and observability
Build AI applications that users can trust and that won't embarrass you at 3 AM. This book gives you the engineering discipline to succeed.
