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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

Beyond Language Models

Insofar, we have only be covering language-specific foundation models, as it also being the focus of this book. Nevertheless, in the context of AI-powered applications, it is worth mentioning that there are additional foundation models that can handle data different from text, that can be embedded and orchestrated.Below you can find a sample of Large Foundation Models available in the market today:

  • Whisper. It is is a general-purpose speech recognition model developed by OpenAI that can transcribe and translate speech in multiple languages. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, spoken language identification, and voice activity detection.
  • Midjourney. Developed by the homonymous independent research lab, Midjourney is based on a Transformer sequence-to-sequence model that takes text prompts and outputs a set of four images that match the prompts. Midjourney...
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