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intermediate ~20 hours
Enterprise Chatbot
Production-grade RAG chatbot with hybrid search, re-ranking, and evaluation metrics for enterprise knowledge bases.
Overview
Production-grade RAG chatbot with hybrid search, re-ranking, and evaluation metrics for enterprise knowledge bases.
Architecture
A intermediate-level project using Python, LangChain, Pinecone, FastAPI, React. The architecture follows a modular design with clear separation between data ingestion, AI processing, and user interface layers.
Tech Stack
Python · LangChain · Pinecone · FastAPI · React
Features
- Hybrid search (BM25 + vector)
- Cross-encoder re-ranking
- Citation tracking
- Evaluation dashboard
Resume Points
- Architected enterprise RAG chatbot serving 10K+ documents with hybrid search
- Implemented re-ranking pipeline improving retrieval precision by 35%
Interview Questions
How would you architect this project for production?
Discuss: API design, error handling, observability (tracing, logging), cost optimization (caching, model routing), security (input validation, rate limiting), and deployment strategy (Docker, CI/CD, auto-scaling).
What were the biggest challenges building this?
Focus on AI-specific challenges: prompt reliability, hallucination handling, latency optimization, cost management, and evaluation of AI output quality.