Car Recommendation Chat Bot
AI-Powered conversational advisor leveraging GPT-3.5 and LangChain.
Overview
An AI chatbot designed to help buyers find their ideal vehicle by processing user preferences, budget, and performance requirements through natural language.
The Problem
Car buyers face information overload when comparing vehicle specs, fuel efficiency, pricing, and features across multiple manufacturers.
Research
Researched prompt engineering strategies, dataset structured search, and LangChain memory modules to maintain context during multi-turn conversations.
The Solution
Engineered an interactive Django web app integrated with OpenAI GPT-3.5 Turbo LLM and LangChain. Queries are matched against a structured automobile dataset for accurate recommendations.
Architecture
Django web server connected to LangChain chain pipelines, OpenAI API gateway, and structured dataset querying.
Challenges
- 01Preventing LLM hallucinations by grounding responses in structured automobile data
- 02Managing API rate limits and optimizing prompt latency for interactive chat
Results
Delivered tailored car recommendations with high user satisfaction during testing, significantly reducing research time for prospective buyers.
Lessons Learned
“Retrieval and grounding with LangChain transforms raw LLMs into accurate domain advisors”
“UI feedback during stream generation improves perceived responsiveness”