Rag with Python Cookbook

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 techniquesUnderstand advanced workflows like semantic-aware chunking and multi-query promptingBuild custom solutions such as chatbots and autonomous agents for specific data challengesContinuously evaluate and optimize systems for accuracy, relevance, and performance

Dominik is a Machine Learning Engineer who has spent years bringing Machine Learning to life in established industry companies like Siemens and Siemens Energy. His career began with researching and applying traditional ML techniques for Forecasting and Anomaly Detection, and has since shifted toward GenAI use cases. Today, Dominik is leading various initiatives across the organization, leveraging Foundation Models and customized RAG systems to improve and automate existing business processes. When he's not driving digital transformation, Dominik shares his expertise through his popular Medium blog, where he simplifies complex Machine Learning concepts into easy-to-digest pieces.