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  • Book Overview & Buying Generative AI Application Integration Patterns
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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

Prompt pre-processing

Before handing off prompts to generative models, pre-processing can make inputs more usable and potentially improve the quality of the outputs.

When thinking about prompt pre-processing, there are two key dimensions that are affected – security and model usability.

On the security aspect, this is the first opportunity to evaluate the prompts and verify that they align with your responsible AI guardrails. Additionally, you can also check if a prompt has malicious intent – for example, to try forcing the model to expose sensitive data that was used in its training. Putting in place content filters, blocklists, and other defenses at this pre-processing stage is important for ensuring security.

The second dimension is related to optimizing model usability. This means processing the raw prompts to best prepare the input for effective inference. As an example, models are unlikely to accept high-fidelity 192 - kHz audio when probably 8 kHz...

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