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Building LLM Powered  Applications

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

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
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Author (1):
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Large Language Models FREE CHAPTER 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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