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Generative AI Application Integration Patterns

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

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781835887608
Length 218 pages
Edition 1st Edition
Languages
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Authors (2):
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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
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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...
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