Category: Generative AI

In healthcare, generative AI is transforming how medical professionals analyze data, summarize clinical notes, and generate insights to improve patient outcomes. From automating medical documentation to assisting in diagnostic reasoning, large language models (LLMs) have the potential to augment clinical workflows and accelerate research. However, these innovations also introduce significant privacy, security, and intellectual property challenges.

Modern generative AI applications often need to stream large language model (LLM) outputs to users in real-time. Instead of waiting for a complete response, streaming delivers partial results as they become available, which significantly improves the user experience for chat interfaces and long-running AI tasks. This post compares three serverless approaches to handle Amazon Bedrock LLM streaming on Amazon Web Services (AWS), which helps you choose the best fit for your application.

Today, AWS announced support for response streaming in Amazon API Gateway to significantly improve the responsiveness of your REST APIs by progressively streaming response payloads back to the client. With this new capability, you can use streamed responses to enhance user experience when building LLM-driven applications (such as AI agents and chatbots), improve time-to-first-byte (TTFB) performance for web and mobile applications, stream large files, and perform long-running operations while reporting incremental progress using protocols such as server-sent events (SSE).

This two-part series explores the different architectural patterns, best practices, code implementations, and design considerations essential for successfully integrating generative AI solutions into both new and existing applications. In this post, we focus on patterns applicable for architecting real-time generative AI applications.