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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

Operations layer

The Operations layer forms the backbone of a robust and efficient GenAI system, ensuring smooth functioning, reliability, and cost-effectiveness. This layer encompasses the critical processes and tools that enable continuous improvement, monitoring, and optimization of your AI models in production environments.

By focusing on CI/CD and MLOps, monitoring and observability, and cost optimization, the Operations layer bridges the gap between development and production, allowing organizations to maintain high-performance AI systems while adapting to changing requirements and managing resources effectively. A well-designed Operations layer is essential for scaling AI solutions, ensuring their reliability, and maximizing the return on investment in GenAI technologies. At the heart of this layer lies the CI/CD pipeline, which streamlines the process of integrating new code and deploying updated models seamlessly. Let’s look at this in a little more detail.

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