# RAG with Python Cookbook. Practical Recipes from Data Preprocessing to LLM Agents

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- Książka: [RAG with Python Cookbook. Practical Recipes from Data Preprocessing to LLM Agents](https://www.kolczanksiazek.pl/dominik-polzer/rag-with-python-cookbook-practical-recipes-from-data-preprocessing-to-llm-agents) ([RAG with Python Cookbook. Practical Recipes from Data Preprocessing to LLM Agents.md](https://www.kolczanksiazek.pl/dominik-polzer/rag-with-python-cookbook-practical-recipes-from-data-preprocessing-to-llm-agents.md))
- Autorzy: [Dominik Polzer](https://www.kolczanksiazek.pl/dominik-polzer)
- Wydawnictwo: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
- ISBN: 979-83-4160-053-9
- Liczba stron: 378
- Język: pl_PL
- Oprawa: Softcover

## Opis

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