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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

Generative AI Batch and Real-Time Integration Patterns

This chapter covers the two primary patterns for designing systems around large language models (LLMs) – batch and real-time. The decision to architect a batch or real-time application will depend on the use case you are working on. In general, batch use cases are formulated around generating data to be consumed later. For example, you will leverage LLMs to extract data points from a large corpus of data and then have a step to generate summaries to be consumed by business analysts on a daily basis. In the case of real-time use cases, data will be used as it becomes available. For example, you will leverage LLMs as online agents to answer questions from your customers or employees through a chat or voice interface.

Diving deeper into batch mode, it involves sending queries in bulk for higher throughput at the cost of latency. This is better suited for long, time-consuming production workloads and large data consumption...

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