I spent a weekend building a Q&A bot for my team's internal docs. It sounded easy: dump PDFs into a vector database, query with embeddings, get answers. Three days later, I had a working prototype — and a healthy respect for all the hidden traps.
The Problem
Our team had 200+ pages of configuration guides scattered across Confluence, Google Docs, and a few dusty PDFs. Every week someone asked "How do we set up the OAuth flow again?" or "What's the default timeout?" I figured a semantic search bot could answer these instantly.
I started simple. Use OpenAI embeddings, store them in Pinecone, then use GPT-4 to generate answers from retrieved chunks. Classic RAG (Retrieval-Augmented Generation).
What I Tried That Didn't Work
