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Total Size:
59.0 MB
Info Hash:
33E3B75498B186D7311266A2C413CE2F6E9A1F34
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Added:
May 1, 2026, 10:21 a.m.
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(Last updated: May 1, 2026, 10:23 a.m.)
| File | Size |
|---|---|
| Polzer D. RAG with Python Cookbook. Practical Recipes from Data...2026.pdf | 17.4 MB |
| Code.zip | 41.7 MB |
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59.0 MB
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2026-05-01
| Uploaded by andryold1 | Size 59.0 MB | Health [ 57 /57 ] | Added 2026-05-01 |
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SOURCE: Polzer D. RAG with Python Cookbook. Practical Recipes from Data...2026
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MEDIAINFO
Textbook in PDF format 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. I see RAG and agentic RAG as a key step toward AI systems that emulate human problem-solving. They actively gather new information, interpret it in context, and continuously plan their next steps based on their findings. By connecting foundation models to external knowledge sources, RAG grounds model outputs in verifiable data and enables systems to reason over trusted information when handling complex tasks. This book is about building production-ready RAG systems. Each recipe focuses on a concrete engineering challenge that appears when moving from prototype to dependable application and explains the trade-offs behind key design decisions. You’ll learn how to design pipelines, select retrieval strategies, evaluate system quality, and operate RAG systems at scale. The goal is to help you build solutions that are accurate, scalable, and maintainable—systems that perform reliably not only in demos but in real-world environments. 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 Who This Book Is For: This book is written for developers and data scientists who want to build practical generative AI applications. You should be comfortable with Python and familiar with APIs, data processing, and general software development. A deep machine learning background is not required. The necessary concepts are introduced as they appear, but you should be ready to write, run, and debug code along the way. About the Author 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
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