menu
{ "item_title" : "Applied RAG - Practical Retrieval-Augmented Generation with Python", "item_author" : [" Dr Balaji Venkateswaran "], "item_description" : "Applied RAG: Practical Retrieval-Augmented Generation with Python, authored by Dr. Balaji Venkateswaran, provides a hands-on blueprint for building reliable, knowledge-grounded AI systems. The book addresses LLM limitations like hallucination and static knowledge, guiding readers through RAG theory, architecture, and practical Python workflows. It covers building blocks such as retrievers and embeddings, advanced retrieval techniques, and integration with frameworks like LangChain. Real-world applications, operational challenges, evaluation, security, and ethical AI are emphasized. This book empowers engineers, scientists, and leaders to master RAG and innovate in creating robust, up-to-date AI solutions for tasks such as QA, summarization, and agents for enterprises.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/890/054/9798900541341_b.jpg", "price_data" : { "retail_price" : "16.99", "online_price" : "16.99", "our_price" : "16.99", "club_price" : "16.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applied RAG - Practical Retrieval-Augmented Generation with Python|Dr Balaji Venkateswaran

Applied RAG - Practical Retrieval-Augmented Generation with Python : A Comprehensive Guide to Building Knowledge-Grounded AI Applications

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Other Available Formats

Paperback
16.99
Hardcover
$26.99

show all formats

Overview

Applied RAG: Practical Retrieval-Augmented Generation with Python, authored by Dr. Balaji Venkateswaran, provides a hands-on blueprint for building reliable, knowledge-grounded AI systems. The book addresses LLM limitations like hallucination and static knowledge, guiding readers through RAG theory, architecture, and practical Python workflows. It covers building blocks such as retrievers and embeddings, advanced retrieval techniques, and integration with frameworks like LangChain. Real-world applications, operational challenges, evaluation, security, and ethical AI are emphasized. This book empowers engineers, scientists, and leaders to master RAG and innovate in creating robust, up-to-date AI solutions for tasks such as QA, summarization, and agents for enterprises.

This item is Non-Returnable

Details

  • ISBN-13: 9798900541341
  • ISBN-10: 9798900541341
  • Publisher: Notion Press
  • Publish Date: September 2025
  • Dimensions: 11 x 8.5 x 0.45 inches
  • Shipping Weight: 1.12 pounds
  • Page Count: 214

Related Categories

You May Also Like...

    1

BAM Customer Reviews