Remove Lambda Remove System Remove System Design
article thumbnail

Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. It also allows for a flexible and modular design, where new LLMs can be quickly plugged into or swapped out from a UI component without disrupting the overall system.

article thumbnail

Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

Alternatively, asynchronous choreography follows an event-driven pattern where agents operate autonomously, triggered by events or state changes in the system. These systems are composed of multiple AI agents that converse with each other or execute complex tasks through a series of choreographed or orchestrated processes.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

Solution overview This section outlines the architecture designed for an email support system using generative AI. High Level System Design The solution consists of the following components: Email service – This component manages incoming and outgoing customer emails, serving as the primary interface for email communications.

article thumbnail

Medical content creation in the age of generative AI

AWS Machine Learning - AI

The system is built upon Amazon Bedrock and leverages LLM capabilities to generate curated medical content for disease awareness. For this reason, our system has been augmented with additional guardrails for fact-checking and rules evaluation. Amazon Lambda : to run the backend code, which encompasses the generative logic.

article thumbnail

Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications

AWS Machine Learning - AI

Parsing documents is important for RAG applications because it enables the system to understand the structure and context of the information contained within the documents. In the next section, we discuss custom processing using Lambda function provided by Knowledge bases for Amazon Bedrock.

article thumbnail

Journey to Event Driven – Part 3: The Affinity Between Events, Streams and Serverless

Confluent

Given that it is at a relatively early stage, developers are still trying to grok the best approach for each cloud vendor and often face the following question: Should I go cloud native with AWS Lambda, GCP functions, etc., The key to event-first systems design is understanding that a series of events captures behavior.

article thumbnail

Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

Ground truth data in AI refers to data that is known to be factual, representing the expected use case outcome for the system being modeled. By providing an expected outcome to measure against, ground truth data unlocks the ability to deterministically evaluate system quality.