Building a production RAG system reveals challenges tutorials often skip. A junior AI engineer documented deploying a document Q&A app, facing critical failures like API quota exhaustion, vector dimension mismatches, and database migration errors. The system, built with FastAPI, React, Pinecone, and Google Gemini, required implementing background tasks for processing and fixing CORS issues.

Key lessons included using fallback providers for embeddings, securing API keys, and performing security audits. The result was a production-hardened system costing just $5/month, demonstrating that production AI is 20% algorithms and 80% infrastructure management. This case study provides essential insights for developers deploying AI to production.