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Building LLM Powered Applications

You're reading from Building LLM Powered Applications

Product type Book

Published in May 2024

Publisher Packt

ISBN-13 9781835462317

Pages 342 pages

Edition 1st Edition

Languages

Concepts

Artificial Intelligence

Author (1):

Valentina Alto

Table of Contents (16) Chapters

Preface

1. Introduction to Large Language Models

2. LLMs for AI-Powered Applications

3. Choosing an LLM for Your Application

4. Prompt Engineering

5. Embedding LLMs within Your Applications

6. Building Conversational Applications

7. Search and Recommendation Engines with LLMs

8. Using LLMs with Structured Data

9. Working with Code

10. Building Multimodal Applications with LLMs

11. Fine-Tuning Large Language Models

12. Responsible AI

13. Emerging Trends and Innovations

14. Other Books You May Enjoy

15. Index

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Qr code Description automatically generated

In this chapter, we are going to cover yet another great capabilities of Large Language Models: the ability to handle structured, tabular data. We will see how, thanks to plug-ins and an agentic approach, we can use LLMs as a natural language interface between us and our structured data, reducing the gap between the business user and the structured information.During this chapter, we will cover the following topics:

Introduction to the main structured data systems
Using tools and plug-ins to connect LLMs to tabular data
Building a Database copilot with LangChain

By the end of this chapter, you will be able to build your own natural language interface towards your data estate, also combining unstructured with structured sources.

The rest of the chapter is locked

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Authors (1)

Valentina Alto

After completing her bachelor's degree in finance, Valentina Alto pursued a master's degree in data science in 2021. She began her professional career at Microsoft as an Azure Solution Specialist, and since 2022, she has been primarily focused on working with Data & AI solutions in the Manufacturing and Pharmaceutical industries. Valentina collaborates closely with system integrators on customer projects, with a particular emphasis on deploying cloud architectures that incorporate modern data platforms, data mesh frameworks, and applications of Machine Learning and Artificial Intelligence. Alongside her academic journey, she has been actively writing technical articles on Statistics, Machine Learning, Deep Learning, and AI for various publications, driven by her passion for AI and Python programming.

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