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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

Summary

In this final chapter of this book, we took a glimpse at the latest advancements in the field of generative AI. We covered new model releases such as OpenAI’s GPT-4V, as well as new frameworks to build LLM-powered applications such as AutoGen. Furthermore, we provided an overview of some companies that are actively powering their business with LLMs, such as Notion and Microsoft.

Generative AI has shown to be the most promising and exciting field of AI, and it has the potential to unleash human creativity, enhance productivity, and solve complex problems. However, as we learned in the previous chapter, it also poses some ethical and social challenges, such as ensuring the quality, safety, and fairness of the generated content, as well as respecting the intellectual property and privacy rights of the original creators. Therefore, as we explore the new horizons of generative AI, we should also be mindful of the implications of our actions in the context of the current...

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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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