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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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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

Use case definition

Let’s consider a scenario where we’re working with a large financial institution that deals with a vast number of legal contracts, regulatory filings, product disclosures, and internal policies and procedures. These documents individually can be into the tens or even hundreds of pages, making it challenging for employees, customers, and other stakeholders to quickly find relevant information when needed. These documents also do not have a consistent format in the way the information is reported, disqualifying non-AI-powered text extractor solutions like regex statements or plain business rules.

The institution wants to implement a chatbot system that can provide a user-friendly interface for users to ask natural language questions and receive concise, relevant answers derived from the organization’s document corpus. This system should leverage the power of RAG to ensure that the generated responses are accurate, contextual, and grounded...

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