Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Retrieval-Augmented Generation

Retrieval-Augmented Generation

Oketunji, Finbarrs

Paperback

DatabasesGeneral Computers

ISBN13: 9798344993683
Publisher: Independently Published
Published: Oct 30 2024
Pages: 392
Weight: 2.77
Height: 1.02 Width: 8.50 Depth: 11.00
Language: English
Retrieval-Augmented Generation (RAG) is the leading pattern for grounding large language models in accurate, up-to-date knowledge. For developers who build assistants, search tools, and document question-answering systems, it combines the fluency of generative models with the reliability of real retrieval.

This book is a practical guide to RAG in Python. It covers the foundations of RAG and synthetic data generation, fast evaluation techniques and metrics, query segmentation and analysis, leading and lagging metrics, advanced retrieval and indexing, query routing, hybrid search, structured data extraction, embedding models, feedback loops, and streaming. It also covers product design, user experience, and advanced prompting, so you can take working code through to production.

Each chapter includes runnable Python examples you can reuse in your own projects, with an emphasis on clean code, accuracy, and the modern RAG tooling of 2026.

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