RAG with Python Cookbook

Practical Recipes from Data Preprocessing to LLM Agents

By: Dominik Polzer

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eText | 28 April 2026 | Edition Number 1

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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