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Generative AI Application Integration Patterns

Generative AI Application Integration Patterns

By : Juan Pablo Bustos, Luis Lopez Soria
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Generative AI Application Integration Patterns

Generative AI Application Integration Patterns

By: Juan Pablo Bustos, Luis Lopez Soria

Overview of this book

Explore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI. With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns. We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought. Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
Table of Contents (13 chapters)
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7
Integration Pattern: Real-Time Intent Classification
11
Other Books You May Enjoy
12
Index

Integration Pattern: Real-Time Retrieval Augmented Generation

In this chapter, we’ll explore another integration pattern that combines the power of Retrieval Augmented Generation (RAG) and generative AI models to build a chatbot capable of answering questions based on the content of PDF files. This approach combines the strengths of both retrieval systems and generative models, allowing us to leverage existing knowledge sources while generating relevant and contextual responses.

One of the key advantages of the RAG approach is its ability to prevent hallucinations and provide better context for the generated responses. Generative AI models, trained on broad data, can sometimes produce responses that are factually incorrect or outdated due to their training data being limited to up to a point in time or they might lack proper context at inference time. By grounding the model’s generation process in relevant information retrieved from a document corpus, the RAG approach...

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