Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Generative AI Application Integration Patterns

You're reading from   Generative AI Application Integration Patterns Integrate large language models into your applications

Arrow left icon
Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835887608
Length 218 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Luis Lopez Soria Luis Lopez Soria
Author Profile Icon Luis Lopez Soria
Luis Lopez Soria
Juan Pablo Bustos Juan Pablo Bustos
Author Profile Icon Juan Pablo Bustos
Juan Pablo Bustos
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction to Generative AI Patterns 2. Identifying Generative AI Use Cases FREE CHAPTER 3. Designing Patterns for Interacting with Generative AI 4. Generative AI Batch and Real-Time Integration Patterns 5. Integration Pattern: Batch Metadata Extraction 6. Integration Pattern: Batch Summarization 7. Integration Pattern: Real-Time Intent Classification 8. Integration Pattern: Real-Time Retrieval Augmented Generation 9. Operationalizing Generative AI Integration Patterns 10. Embedding Responsible AI into Your GenAI Applications 11. Other Books You May Enjoy
12. Index

Data layer

The Data layer is the bedrock upon which your GenAI systems are built. It’s not just about having data; it’s about managing it effectively to ensure the quality, security, and ethical use of information. Robust data management processes are non-negotiable. Your GenAI systems are only as good as the data they interact with. For example, without enough contextual information, Large Language Models (LLMs) can hallucinate, and too much noise could cause the model to lose information in the middle, as described in the document Lost in the Middle: How Language Models Use Long Contexts (https://arxiv.org/abs/2307.03172). Therefore, you want to make a conscious effort to build and scale your data pipelines (RAG and fine-tuning) to feed the right level of detail and content to enhance your GenAI model’s abilities.

We are going to provide an overview of the high-level components to keep in mind when preparing your data:

  • Data quality: Implement...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime