Building Event-Driven GenAI Applications in 4 Steps

This post outlines a four-step methodology for building event-driven Generative AI applications, focusing on data augmentation, inference, workflows, and post-processing. It emphasizes the use of Apache Kafka and data streaming platforms to integrate disparate data sources, create embeddings for vector stores, and decouple LLM inference from user-facing applications. The approach leverages event-driven patterns to manage complexity, enable independent scaling of teams and services, and treat AI components as modular, substitutable parts. Specific technical aspects discussed include using Kafka consumers to pull data from connectors, process it, and create embeddings for vector stores, and treating LLM calls as asynchronous operations to avoid blocking web application threads. The post also touches upon using LangChain for workflow composition and the importance of post-processing for validating LLM outputs.