🚀 Building a Serverless GenAI Chatbot using Amazon Bedrock & Amazon Kendra
Generative AI becomes truly powerful when combined with enterprise knowledge.
In this hands-on workshop, I built a fully serverless chatbot using Amazon Bedrock, Amazon Kendra, and Retrieval-Augmented Generation (RAG).
❓ Why RAG?
LLMs are powerful — but they don’t know your data.
Retrieval-Augmented Generation (RAG) bridges this gap by:
- Retrieving relevant enterprise documents
- Injecting context into prompts
- Producing accurate, grounded responses
🧠 Architecture Breakdown
Core Components:
- Frontend: AWS Amplify (Vue.js)
- API Layer: Amazon API Gateway
- Compute: AWS Lambda
- AI Models: Amazon Bedrock (Claude 3, Mistral, Llama)
- Search: Amazon Kendra
- Storage: Amazon S3
- Security: Amazon Cognito
🔄 End-to-End Flow
- User submits a query
- Lambda retrieves relevant documents
- Prompt is augmented with context
- Bedrock generates a grounded response
- UI displays the result
🛠️ What I Implemented
✔ CloudFormation-based infrastructure
✔ AWS SAM backend deployment
✔ Bedrock LLM integration
✔ Kendra document indexing
✔ Secure authentication via Cognito
✔ Serverless frontend with Amplify
💡 Real-World Applications
- Internal enterprise assistants
- Compliance & policy search
- Technical documentation bots
- Customer support automation
- Knowledge discovery platforms
📌 Key Learnings
- RAG dramatically improves LLM accuracy
- Bedrock abstracts LLM complexity
- Kendra simplifies enterprise search
- Serverless = scale + cost efficiency
🔜 What’s Next?
- Multi-tenant SaaS architecture
- Agent-based workflows
- Streaming token responses
- Cost & latency optimization
🔗 Resources
- GitHub Repo: https://github.com/subhashbohra/aws-serverless-labs/tree/main/01-bedrock-kendra-chatbot
- AWS Workshops: https://workshops.aws.com
- Blog: https://acloudresume.com
Thanks for reading!
If you’re exploring AWS Serverless + GenAI, let’s connect 🚀
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