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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

Implementing an LLM-powered recommendation system

Now that we have covered some theory about recommendation systems and emerging research on how LLMs can enhance them, let’s start building our recommendation app, which will be a movie recommender system called MovieHarbor. The goal will be to make it as general as possible, meaning that we want our app to be able to address various recommendations tasks with a conversational interface. The scenario we are going to simulate will be that of the so called “cold-start”, that means the first interaction of an user with the recommendation system, so that we do not have user’s preference history. On the other hand, we will leverage a movie database with textual description.With this purpose, we will use the “Movie recommendation data” dataset, available on Kaggle at https://www.kaggle.com/datasets/rohan4050/movie-recommendation-data.The idea is that of having a dataset with a textual description of each...

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