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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

Comparing the three options

We examined three options to achieve this result: options 1 and 2 follow the “agentic” approach, using, respectively, pre-built toolkit and single tools combined; option 3, on the other hand, follows a hard-coded approach, letting the developer decide the order of actions to be done.

All three come with pros and cons, so let’s wrap up some final considerations:

  • Flexibility vs control: The agentic approach lets the LLM decide which actions to take and in which order. This implies greater flexibility for the end user since there are no constraints in terms of queries that can be done. On the other hand, having no control over the agent’s chain of thoughts could lead to mistakes that would need several tests of prompt engineering. Plus, as LLMs are non-deterministic, it is also hard to recreate mistakes to retrieve the wrong thought process. Under this point of view, the hard-coded approach is safer, since the developer...
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