Building an Internal AI Assistant on AWS with Amazon Bedrock and Managed RAG

Building an internal AI assistant on the Amazon Web Services (AWS) platform involves a range of AWS services to create a comprehensive and secure system. This assistant allows authenticated staff members to interact with an Artificial Intelligence (AI) system using the organization's internal documents as its knowledge source. The system employs Amazon Bedrock Knowledge Bases for Retrieval Augmented Generation (RAG), which combines the AI model's language generation capability with the organization's contextual documents for more accurate and relevant responses.

The project utilized various AWS services to build a complete application around the AI model rather than merely connecting an application to an LLM. Staff members begin by logging in through Amazon Cognito, which manages user authentication. Amazon API Gateway then validates these requests, ensuring only authorized personnel can interact with the AI assistant.

Amazon Lambda serves as the application's backend logic, orchestrating the AI workflow when relevant information is retrieved from the organization's documents via a managed Bedrock Knowledge Base in Amazon S3. This process ensures that the AI model generates responses grounded in the organization's specific information, rather than relying solely on general knowledge.

Moreover, the system incorporates Amazon Bedrock's Guardrails to manage model input and output, ensuring secure and controlled AI interactions. Amazon CloudWatch is used for logging and monitoring the execution of Lambda functions, providing visibility into the system's operation. AWS IAM (Identity and Access Management) is employed for permissions and access control, ensuring that only authenticated users can access the system and its functionalities.

Infrastructure provisioning is managed through Terraform, allowing for consistent and repeatable deployment of the system. By integrating multiple AWS services as part of a unified system, the project provided practical experience in working with AWS, from hosting a static frontend with AWS Amplify to applying Bedrock Guardrails to AI requests and responses.

The entire system was designed with a human review process in mind, where the assistant generates drafts and responses for staff members to review, ensuring the final output remains under human control.