In Store Pick Up and Membership Service are currently unavailable.
Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Generative AI, such as Large Language Models (LLMs) possess immense potential. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional "hallucinations."
With this book, you’ll go from preparing the environment to gradually adding features and deploying the final project. You’ll gradually progress from fundamental LLM concepts to exploring the features of this framework. Practical examples will guide you through essential steps for personalizing and launching your LlamaIndex projects. Additionally, you’ll overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases, covering Generative AI and LLM, as well as LlamaIndex deployment. As you approach the conclusion, you’ll delve into customization, gaining a holistic grasp of LlamaIndex's capabilities and applications.
By the end of the book, you’ll be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.

1145420686
Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Generative AI, such as Large Language Models (LLMs) possess immense potential. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional "hallucinations."
With this book, you’ll go from preparing the environment to gradually adding features and deploying the final project. You’ll gradually progress from fundamental LLM concepts to exploring the features of this framework. Practical examples will guide you through essential steps for personalizing and launching your LlamaIndex projects. Additionally, you’ll overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases, covering Generative AI and LLM, as well as LlamaIndex deployment. As you approach the conclusion, you’ll delve into customization, gaining a holistic grasp of LlamaIndex's capabilities and applications.
By the end of the book, you’ll be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.

35.99 In Stock
Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

by Andrei Gheorghiu
Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications
Add to Wishlist
Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

by Andrei Gheorghiu

Overview

Generative AI, such as Large Language Models (LLMs) possess immense potential. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional "hallucinations."
With this book, you’ll go from preparing the environment to gradually adding features and deploying the final project. You’ll gradually progress from fundamental LLM concepts to exploring the features of this framework. Practical examples will guide you through essential steps for personalizing and launching your LlamaIndex projects. Additionally, you’ll overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases, covering Generative AI and LLM, as well as LlamaIndex deployment. As you approach the conclusion, you’ll delve into customization, gaining a holistic grasp of LlamaIndex's capabilities and applications.
By the end of the book, you’ll be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.

Product Details

ISBN-13: 9781805124405
Publisher: Packt Publishing
Publication date: 05/10/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 368
File size: 9 MB

About the Author

Andrei Gheorghiu is an IT consultant and trainer with experience in generative AI. He developed the "Prompt Engineering for Everyone" course and has researched the LlamaIndex ecosystem, experimenting with its features and applications. His recent work includes using Generative AI to translate educational materials and testing virtual assistants for classroom use. With over 20 years in the IT industry, Andrei also holds certifications like ITIL Master and has trained more than 15,000 students.

Table of Contents

Table of Contents
  1. Understanding Large Language Models
  2. LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem
  3. Kickstarting Your Journey with LlamaIndex
  4. Ingesting Data into Our RAG Workflow
  5. Indexing with LlamaIndex
  6. Querying Our Data, Part 1 – Context Retrieval
  7. Querying Our Data, Part 2 – Postprocessing and Response Synthesis
  8. Building Chatbots and Agents with LlamaIndex
  9. Customizing and Deploying Our LlamaIndex Project
  10. Prompt Engineering Guidelines and Best Practices
  11. Conclusions and Additional Resources
From the B&N Reads Blog
Page 1 of

Related Subjects

Computers
Computer Programming
Programming - General & Miscellaneous
Computers
Computer Programming
Programming - General & Miscellaneous

Customer Reviews