Chatbot (LoRA + RAG)

STAR Summary

Situation

The project needed to show both sides of a tourism chatbot decision: a custom fine-tuned model that could learn destination voice and a production-ready managed model that could answer quickly with citations.

Task

Built the prototype from crawl to deployment, then expanded it into a dual-backend demo comparing a custom LoRA/SageMaker model with a live Bedrock model.

Action

• Crawled Visit Grand Junction pages, built a FAISS retrieval index, and generated a tourism-specific Q&A dataset with an open-source LLM through Ollama.
• Fine-tuned a Mistral 7B/Qwen-style chatbot path with LoRA and deployed the custom model behind AWS SageMaker/Lambda for cold-start and model-control comparison.
• Added a Bedrock backend as the default live path, keeping the same RAG/citation experience while providing faster always-available responses for the website demo.

Result

• The project now demonstrates both architectures: Bedrock as the practical live production path and LoRA/SageMaker as the custom fine-tuning path.
• Viewers can compare managed-model reliability, streaming responses, and citation behavior against the custom model's cold-start and deployment tradeoffs.
• The demo keeps source-grounded answers and citation links while making the model choice transparent through the backend selector.