Unlocking data with generative AI and RAG : (Record no. 141574)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02041nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260722143906.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260722s2024 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781835887905 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | PK-LaUMT |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Item number | BOU-U |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Bourne, Keith |
| 245 10 - TITLE STATEMENT | |
| Title | Unlocking data with generative AI and RAG : |
| Remainder of title | enhance generative AI systems by integrating internal data with large language models using RAG / |
| Statement of responsibility, etc | Keith Bourne |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Birmingham : |
| Name of publisher, distributor, etc | Packt Publishing, |
| Date of publication, distribution, etc | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxii, 323 p. |
| 500 ## - GENERAL NOTE | |
| General note | Index present |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "Generative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes. The book explores RAG's role in enhancing organizational operations by blending theoretical foundations with practical techniques. You'll work with detailed coding examples using tools such as LangChain and Chroma's vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG's diverse use cases, from search engines to chatbots. You'll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quatitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies. By the end of this book, you'll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what's possible with this revolutionary AI technique"--Cover, page 4 |
| 546 ## - LANGUAGE NOTE | |
| Language note | Eng |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Generative-AI |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Large language models-AI |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|
| UMT Main Campus | UMT Main Campus | 2026-07-22 | 006.3 BOU-U | 153377 | 2026-07-22 | 2026-07-22 | Books |