You're reading from Building LLM Powered Applications Create intelligent apps and agents with large language models

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781835462317

Length 342 pages

Edition 1st Edition

Languages

Python

Tools

Falcon

Concepts

Artificial Intelligence

Author (1):

Valentina Alto

View More author details

Table of Contents (16) Chapters

Preface

Who this book is for
What this book covers
To get the most out of this book
Get in touch

1. Introduction to Large Language Models FREE CHAPTER

What are large foundation models and LLMs?
Most popular LLM transformers-based architectures
Training and evaluating LLMs
Base models versus customized models
Summary
References

2. LLMs for AI-Powered Applications

How LLMs are changing software development
The copilot system
Introducing AI orchestrators to embed LLMs into applications
Summary
References

3. Choosing an LLM for Your Application

The most promising LLMs in the market
Beyond language models
A decision framework to pick the right LLM
Summary
References

4. Prompt Engineering

Technical requirements
What is prompt engineering?
Principles of prompt engineering
Advanced techniques
Summary
References

5. Embedding LLMs within Your Applications

Technical requirements
A brief note about LangChain
Getting started with LangChain
Working with LLMs via the Hugging Face Hub
Summary
References

6. Building Conversational Applications

Technical requirements
Getting started with conversational applications
Developing the front-end with Streamlit
Summary
References

7. Search and Recommendation Engines with LLMs

Technical requirements
Introduction to recommendation systems
Existing recommendation systems
How LLMs are changing recommendation systems
Implementing an LLM-powered recommendation system
Developing the front-end with Streamlit
Summary
References

8. Using LLMs with Structured Data

Technical requirements
What is structured data?
Getting started with relational databases
Implementing the DBCopilot with LangChain
Developing the front-end with Streamlit
Summary
References

9. Working with Code

Technical requirements
Choosing the right LLM for code
Code understanding and generation
Act as an algorithm
Leveraging Code Interpreter
Summary
References

10. Building Multimodal Applications with LLMs

Technical requirements
Why multimodality?
Building a multimodal agent with LangChain
Option 1: Using an out-of-the-box toolkit for Azure AI Services
Option 2: Combining single tools into one agent
Option 3: Hard-coded approach with a sequential chain
Comparing the three options
Developing the front-end with Streamlit
Summary
References

11. Fine-Tuning Large Language Models

Technical requirements
What is fine-tuning?
When is fine-tuning necessary?
Getting started with fine-tuning
Summary
References

12. Responsible AI

What is Responsible AI and why do we need it?
Responsible AI architecture
Regulations surrounding Responsible AI
Summary
References

13. Emerging Trends and Innovations

The latest trends in language models and generative AI
Companies embracing generative AI
Summary
References

14. Other Books You May Enjoy

15. Index

Technical requirements

None.

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Table of Contents (16) Chapters

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Table of Contents (16) Chapters