Build an AI-powered website assistant with Amazon Bedrock

2025-12-29 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, medium

Summary

This post details how to construct an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases to address the challenge of customers needing fast answers and support teams being overwhelmed. The solution employs Retrieval-Augmented Generation (RAG) to retrieve information from a knowledge base, providing instant, relevant answers for customers and a powerful retrieval system for support agents, reducing resolution times and offering round-the-clock automated support. Key components include Amazon Bedrock Knowledge Bases for content ingestion from websites and Amazon S3, Amazon Bedrock managed LLMs (specifically Amazon Nova Lite and Amazon Titan Text Embeddings V2) for response generation, and a scalable serverless architecture utilizing Amazon ECS, AWS Lambda, and AWS CDK for CI/CD deployment. The system supports differentiated access, allowing internal users to access both public and internal documentation, while external users are restricted to public content.

Key takeaway

For MLOps Engineers or AI Engineers building customer support solutions, this guide demonstrates a practical, scalable architecture. You should consider implementing Amazon Bedrock Knowledge Bases with web crawling and S3 data sources to create a RAG-powered assistant. This approach allows for differentiated access to internal and external documentation, significantly improving response times and agent efficiency while maintaining data security. Leverage AWS CDK for streamlined deployment and management of the solution.

Key insights

An AI-powered RAG system on AWS Bedrock can provide instant, role-based support from diverse data sources.

Principles

• RAG enhances LLM responses with relevant, retrieved information.
• Role-based access controls can filter knowledge base content.
• Serverless architectures enable scalable AI solutions.

Method

The solution involves creating an Amazon Bedrock Knowledge Base, ingesting data via web crawling and S3 uploads, configuring Amazon Titan Text Embeddings V2, deploying infrastructure with AWS CDK, and using Amazon Nova Lite for query responses with user-type filtering.

In practice

• Use Amazon Bedrock Knowledge Bases for diverse data ingestion.
• Implement data source IDs for access control.
• Deploy with AWS CDK for automated CI/CD.

Topics

• Amazon Bedrock
• Retrieval-Augmented Generation
• Customer Support AI
• Knowledge Bases
• Serverless Architecture

Code references

• aws-samples/sample-aipowered-web-using-amazon-bedrock

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.