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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

Summary

In this chapter, we dwelved into the new way of developing applications that LLMs have been paving, introducing the concept of copilot and encouraging the emerging of new AI orchestrators. Among those, we focused on three projects - LangChain, HayStack and Semantic Kernel – and we examined their features, main components and some criteria to decide which one to pick.Once decided the AI orchestrator, another pivotal step is to decide which LLM(s) we want to embed into our applications. In next Chapter, we are going to see the most prominent LLMs in the market today – both proprietary and open-source – and understand some decision criteria to pick the proper models with respect to the application use cases.

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