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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

Introducing AI orchestrators to embed LLMs into applications

Earlier in this chapter, we saw that there are two main aspects to consider when incorporating LLMs within applications: a technical aspect and a conceptual aspect. While we can explain the conceptual aspect with the brand-new category of software called Copilot, in this section, we are going to further explore how to technically embed and orchestrate LLMs within our applications.

The main components of AI orchestrators

From one side, the paradigm shift of foundation models implies a great simplification in the domain of AI-powered applications: after producing models, now the trend is consuming models. On the other side, many roadblocks might arise in developing this new kind of AI, since there are LLM-related components that are brand new and have never been managed before within an application life cycle. For example, there might be malicious actors that could try to change the LLM instructions (the system message...

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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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