RAG Chatbot with Claude

STEP 1

Create a serverless, React-based chatbot using Claude on Bedrock with RAG capabilities for PDF documents.

How to use the RAG Chatbot with Claude AI Prompt

Overview: This template generates a comprehensive, production-ready architectural blueprint for deploying a sophisticated Retrieval-Augmented Generation (RAG) chatbot. It leverages the power of Claude 3 Sonnet via Amazon Bedrock, integrated with a serverless AWS backend (Lambda/API Gateway) and a vector database to enable intelligent querying over private PDF documents stored in S3. The output focuses on delivering high performance, security, and cost efficiency, adhering strictly to AWS Well-Architected principles.

Who is this for:

  • AI Solutions Architects designing enterprise-grade GenAI applications.
  • Backend Developers tasked with building scalable, serverless data pipelines for document ingestion and querying.
  • Technical Project Managers requiring detailed cost breakdowns and deployment documentation for RAG systems.

How it works: The prompt acts as a detailed technical specification request. It mandates specific technologies (Claude 3 Sonnet, React, Terraform, OpenSearch/Vector DB) and imposes strict non-functional requirements (performance under 2s, cost under $200/month). The AI model is instructed to synthesize these requirements into a complete deliverable package, including architecture diagrams, IaC scripts, security plans, and cost estimates, ensuring all components work together seamlessly for real-time, document-aware chat.

Pro-Tip: To maximize the quality of the Terraform output, explicitly state the preferred vector database (e.g., "Use Amazon OpenSearch Serverless for the vector store") in a follow-up prompt, as the initial prompt allows for an "equivalent." Always request the architecture diagram description first, as it sets the context for the subsequent detailed deliverables.

# Generative AI
# ServerLess

Original Prompt Template

# AWS Claude RAG Chatbot Architecture Design Request ## Project Overview I need a comprehensive design for a web-based chatbot application with the following key components: - Claude 3 Sonnet on Amazon Bedrock as the LLM - RAG capabilities for PDF documents stored in S3 - React frontend with real-time chat functionality ## Detailed Requirements ### 1. Core Functionality - **User Interface**: Web-based chat interface built with React - **AI Backend**: Claude 3 Sonnet model via Amazon Bedrock API - **RAG System**: - PDF document search and retrieval from S3 - Document upload functionality for expanding knowledge base - Vector search across 1000+ documents - **Persistence**: - Chat history storage and retrieval - User authentication and session management ### 2. Performance Requirements - Support for 100 concurrent users - Response times under 2 seconds for typical queries - Ability to process and index documents up to 100MB each ### 3. Cost Optimization Targets - Monthly operational cost under $200 for moderate usage - Strategic use of spot instances where appropriate - Caching implementation to minimize Bedrock API calls - Pay-per-use services prioritized ### 4. Technical Architecture Preferences - Serverless backend architecture (AWS Lambda) - Vector database for embeddings (OpenSearch or equivalent) - PDF processing pipeline for text extraction and embedding - WebSocket implementation for real-time chat experience - API Gateway for REST endpoint management ### 5. Security & Compliance Requirements - End-to-end encryption for documents (at rest and in transit) - IAM roles configured with least privilege principle - Rate limiting implementation to prevent system abuse - Comprehensive audit logging for all system interactions ## Deliverables Requested 1. Complete AWS solution architecture diagram 2. Infrastructure as Code (Terraform preferred) 3. Detailed deployment guide with step-by-step instructions 4. Cost estimation breakdown by AWS service 5. Security implementation details 6. Readme with full documentation Please provide a solution that adheres to AWS Well-Architected Framework principles, with particular attention to reliability, performance efficiency, and cost optimization. Provide your complete solution architecture without any preamble, starting with the high-level architecture diagram description.