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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 2. LLMs for AI-Powered Applications FREE CHAPTER 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 introduced the concept of multimodality and how to achieve it even without multimodal models. We explored three different ways of achieving the objective of a multimodal application: an agentic approach with a pre-built toolkit, an agentic approach with the combination of single tools, and a hard-coded approach with chained models.

We delved into the concrete implementation of three applications with the above methods, examining the pros and cons of each approach. We saw, for example, how an agentic approach gives higher flexibility to the end user at the price of less control of the backend plan of action.

Finally, we implemented a front-end with Streamlit to build a consumable application with the hard-coded approach.

With this chapter, we conclude Part 2 of the book, where we examined hands-on scenarios and built LLMs-powered applications. In the next chapter, we will focus on how to customize your LLMs even more with the process of fine...

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