| 000 | 02217nam a22002297a 4500 | ||
|---|---|---|---|
| 005 | 20261019160013.0 | ||
| 008 | 261019b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9798277349045 | ||
| 028 |
_a: Savera Book Distributors _b: 4754 Akarshan Bhawan, 23 Ansari Road, Darya Ganj, New Delhi _c: 26-27/CRB/756 _d: 19/10/2026 |
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| 082 | _a006.3 MAZ | ||
| 100 | _aMazzanti, Oscar. | ||
| 245 |
_aGenerative AI and RAG for Beginners _b: A Practical Step-by-Step Guide to Building LLM and RAG Applications with LangChain and Python. _c/ Oscar Mazzanti. |
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| 260 |
_b: Peacockbooks _c, 2025. |
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| 300 |
_a254p. _c: 28cm. |
||
| 365 | _b1740 | ||
| 520 | _aAre you ready to dive into the world of Artificial Intelligence and build your own intelligent applications? Generative AI and RAG for Beginners is the perfect starting point for anyone eager to explore how large language models (LLMs) and Retrieval-Augmented Generation (RAG) work in the real world. This practical, hands-on guide will walk you through the process of creating your very own AI-powered solutions using LangChain and Python. Whether you're a complete beginner or looking to enhance your existing knowledge, this book breaks down complex concepts into simple, actionable steps. You'll learn how to: Build powerful AI applications that retrieve accurate information and generate contextually aware responses. Master LangChain and Python to create efficient workflows, connect databases, and integrate external data sources. Understand RAG systems and how they enhance the capabilities of traditional language models by incorporating real-world knowledge. Navigate real-world challenges with expert tips on improving performance, optimizing retrieval pipelines, and handling large datasets. Create practical AI solutions for industries like business, education, research, and more! Packed with clear explanations, code examples, and project-based learning, this guide will help you develop a strong foundation in AI development and prepare you for future challenges in machine learning and natural language processing. | ||
| 650 | _aAI and RAG | ||
| 650 | _aAI | ||
| 650 | _aBuilding LLM and RAG | ||
| 700 | _aOscar Mazzanti. | ||
| 942 | _cBOOK | ||
| 999 |
_c70911 _d70911 |
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