Rag with Python Cookbook: Practical Recipes from Data Preprocessing to LLM Agents

As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.

Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.

• Learn core RAG components including embedding, retrieval, and generation techniques
• Understand advanced workflows like semantic-aware chunking and multi-query prompting
• Build custom solutions such as chatbots and autonomous agents for specific data challenges
• Continuously evaluate and optimize systems for accuracy, relevance, and performance
  
  Author: Dominik Polzer
  Binding Type: Paperback
  Publisher: O'Reilly Media
  Published: 06/02/2026
  Pages: 375
  Weight: 1.32lbs
  Size: 9.19h x 7.00w x 0.78d
  ISBN: 9798341600560