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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

Logging

Establishing comprehensive observability through logging is critical when integrating GenAI models into applications. Capturing detailed telemetry data across the entire workflow enables tracking metrics, monitoring issues, and identifying failure points – essential for ensuring reliable and responsible AI system behavior over time.

Detailed usage metrics and logging provide a wealth of observability benefits at the front-end integration points for GenAI. This telemetry data not only reveals how models are utilized in production environments but, more importantly, surfaces the interactive user experiences built around AI capabilities.

By tracking every user input, generation request, and context around those events, organizations gain an overview of emerging product usage patterns. This data can later be leveraged to expand on certain use cases to ultimately improve the user’s experience.

Questions like “Are certain demographics or geographies...

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