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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
Author (1):
Valentina Alto Valentina Alto
Profile icon Valentina Alto
Toc

Table of Contents (16) Chapters close

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

Responsible AI architecture

Generally speaking, there are many levels at which we can intervene to make a whole LLM-powered application safer and more robust: the model level, the metaprompt level, and the user interface level. This architecture can be illustrated as follows:

Figure 12.1: Illustration of different mitigation layers for LLM-powered applications

Of course, it is not always possible to work at all levels. For example, in the case of ChatGPT, we consume a pre-built application with a black-box model and a fixed UX, so we have little room for intervention only at the metaprompt level. On the other hand, if we leverage open-source models via an API, we can act up to the model level to incorporate Responsible AI principles. Let’s now see a description of each layer of mitigation.

Model level

The very first level is the model itself, which is impacted by the training dataset we train it with. In fact, if the training data is biased, the model will...

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