Technical How-to | Integration & Automation

Enhance customer experience with an integrated AI assistant

In this post, we demonstrate how to build an enterprise AI assistant solution that uses LLMs in Amazon Bedrock with the precision of enterprise knowledge bases using the RAG approach. By integrating AWS services such as Lambda and Amazon Bedrock, our solution enables organizations to securely access and retrieve proprietary data, providing contextually relevant and accurate responses. The RAG approach not only enhances the assistant’s ability to provide tailored responses within specific enterprise data domains, but also mitigates the risk of hallucinations. By injecting the latest enterprise proprietary knowledge into the response generation context, our solution makes sure that the assistant remains up-to-date and adaptable to evolving specific business needs. The sample code repository and CloudFormation template can enable organizations to streamline the development and deployment of their RAG-based AI assistant solutions.

Build an automated deployment of generative AI with agent lifecycle changes using Terraform

This blog post guides you through implementing a generative AI intelligent chatbot using Amazon Bedrock and AWS services, including developer lifecycle changes. It covers creating a scalable chatbot infrastructure with Terraform, using services like Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, OpenSearch Service, Lambda, Amazon S3, and IAM. The focus on IaC and automation brings key DevOps benefits: version control, consistent deployments, reduced errors, faster provisioning, and improved collaboration.