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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

Most promising LLMs in the market

The last few months have witnessed an unprecedented surge in the research and development of LLMs. Several new models have been released or announced by different organizations, each with its own features and capabilities. Some of these models are the largest and most advanced ever created, surpassing the previous state-of-the-art by orders of magnitude. Others are lighter yet more specialized on specific tasks.In this chapter, we will review some of the most promising LLMs in the market as of 2023. We will introduce their background, key findings, and main techniques. We will also compare their performance, strengths, and limitations on various benchmarks and tasks. We will also discuss their potential applications, challenges, and implications for the future of AI and society.

Proprietary models

Proprietary LLMs are developed and owned by private companies, and they are not disclosed with code. They are also typically subject to a fee for consumption...

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