Build production-minded AI applications with Python, from your first LLM API call to retrieval-augmented generation and reliable agent workflows.
This hands-on guide shows you how to design, implement, evaluate, secure, and operate practical LLM systems. You will learn structured outputs, tool calling, embeddings, vector search, knowledge-base question answering, memory, planning, multi-agent coordination, observability, testing, cost control, and deployment. Each chapter connects working Python examples to the engineering decisions that make AI software dependable.
Inside you will learn how to:
• integrate LLM APIs with typed, maintainable Python code
• build RAG pipelines and knowledge-base Q&A systems
• create tool-using agents with explicit safety boundaries
• evaluate quality, latency, reliability, and cost
• add logging, tracing, retries, permissions, and human approval
• move from prototypes to production-ready services
Written for Python developers, AI application engineers, technical leads, and motivated beginners who want an end-to-end path from experimentation to responsible automation.
