Car Recommendation Chat Bot
Conversational automobile recommendation advisor using GPT-3.5 Turbo and LangChain.
Overview
Built a Django-based conversational application integrating GPT-3.5 Turbo with LangChain to process user queries against a structured automobile dataset and generate vehicle recommendations.
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 context pipelines to process user preferences during multi-turn conversations.
The Solution
Engineered an interactive Django web app integrated with OpenAI GPT-3.5 Turbo LLM and LangChain. User queries are matched against a structured automobile dataset for conversational recommendations.
Architecture
Django web server connected to LangChain chain pipelines, OpenAI API gateway, and structured dataset querying.
Challenges
- 01Grounding LLM responses in structured automobile dataset records to avoid invalid specs
- 02Managing API rate limits and optimizing prompt latency for interactive chat
Results
Delivered tailored car recommendations through a conversational interface, demonstrating effective integration of LLM APIs with structured datasets.
Lessons Learned
“Retrieval and prompt grounding with LangChain transforms raw LLMs into accurate domain advisors”
“UI feedback during stream generation improves perceived responsiveness”