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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

Existing recommendation systems

Modern recommendation systems uses Machine Learning (ML) techniques to make better predictions about user’s preferences, based on the available data that can come from:

  • User behavior datainsights about user interaction with a product. This data can be acquired from factors like user ratings, clicks, and purchase records.
  • User demographic data refers to personal information about users, including details like age, educational background, income level, and geographical location.
  • Product attribute data involves information about the characteristics of a product, such as the genre for books, cast for movies, or cuisine for food."

As of today, some of the most popular ML rechniques are K-nearest neighbors, dimensionality reduction and Neural networks.

K-Nearest Neighbors

K-nearest neighbor (KNN) is a machine learning algorithm that can be used for both classification and regression problems. It works by finding the k closest data points to a new...

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