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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

Option 2: Combining single tools into one agent

In this leg of our journey toward multimodality, we will leverage different tools as plug-ins to our STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent. Our goal is to build a copilot agent that will help us generate reviews about YouTube videos, as well as post those reviews on our social media with a nice description and related picture. In all of that, we want to make little or no effort, so we need our agent to perform the following steps:

Search and transcribe a YouTube video based on our input.
Based on the transcription, generate a review with a length and style defined by the user query.
Generate an image related to the video and the review.

We will call our copilot GPTuber. In the following subsections, we will examine each tool and then put them all together.

YouTube tools and Whisper

The first step of our agent will be to search and transcribe the YouTube video based on our input. To...

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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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