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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

Summary

In this chapter, we explored the application of GenAI to summarize client applications in the financial services industry. We kicked things off by defining the problem statement, where financial institutions need to efficiently review and distill key information from lengthy client applications while ensuring regulatory compliance.

We highlighted the value of GenAI-driven summarization in this context, enabling the extraction of salient details, streamlining downstream processes, and facilitating better-informed decision-making while adhering to compliance standards.

Next, we proposed a cloud-native, serverless architecture on Google Cloud to handle the batch processing of client applications for summarization. This scalable setup leverages various services, including Cloud Storage, Pub/Sub, Cloud Functions, and databases like BigQuery or Cloud Firestore, allowing for seamless integration of the AI model and storage of the generated summaries.

We then delved into...

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