Introducing generative AI integration patterns

Let’s now assume you already have a promising use case in mind. As I’m sure you would agree, clearly defining the use case is critical before proceeding further. You’ve already identified which foundational model provides acceptable performance for your needs. So now you’re starting to consider how GenAI fits into the application development process.

At a high level, there are two main workflows for integrating applications with GenAI. One is real time, where you’ll typically interact with an end user or AI agent, providing responses as prompts come in. The second is batch processing, where requirements are bundled up and processed in groups (batches).

A prime example of a real-time workflow would be a chatbot. Here, prompts from the user are processed and then sent to the model and the responses are returned immediately, as you need to consume the outputs without delay. On the other hand, consider a data enrichment use case for batch processing. You could collect multiple data points over time for later consumption after being enriched by the model in batches.

In this book, we will explore these integration patterns through practical examples. This will help you to obtain hands-on experience with GenAI-driven applications and allow you to integrate these patterns in your own use cases.

By “integration pattern,” we refer to a standardized architectural approach for incorporating a technology into your application or system. In this context, integration patterns provide proven methods for connecting generative AI models to real-world software.

There are a few key reasons why we need integration patterns when working with generative AI:

• Time savings: Following established patterns allows developers to avoid reinventing the wheel for common integration challenges. This accelerates time to value.
• Improving quality: Leveraging best practices encoded in integration patterns leads to more robust, production-grade integrations. Things like scalability, security, and reliability are top of mind.
• Reducing risk: Well-defined integration patterns enable developers to mitigate risks around performance, costs, and other pitfalls that can emerge when integrating new technologies.

Overall, integration patterns deliver templates and guardrails, so developers don’t have to start integration efforts from scratch. By relying on proven blueprints, readers can integrate generative AI more efficiently while avoiding common mistakes. This speeds up development cycles significantly and sets integrations up for long-term success.