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beginner ~6 hours
PDF Chat
Upload PDFs and chat with their contents using RAG — the quintessential LLM engineering project.
Overview
Upload PDFs and chat with their contents using RAG — the quintessential LLM engineering project.
Architecture
A beginner-level project using Python, LangChain, ChromaDB, Streamlit. The architecture follows a modular design with clear separation between data ingestion, AI processing, and user interface layers.
Tech Stack
Python · LangChain · ChromaDB · Streamlit
Features
- PDF upload
- Chunking pipeline
- Semantic search
- Streaming chat
Resume Points
- Developed a RAG-based PDF chat application with ChromaDB vector store
- Optimized chunking strategy improving answer relevance by 40%
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.