RAG for Small Business: A Plain-English Guide

RAG stands for Retrieval-Augmented Generation. Ignore the acronym. Here's what it actually does:

You have 500 pages of SOPs, product docs, and past proposals. Instead of training a model on them (expensive, slow, requires ML expertise), you index them so an AI can look things up in real time before answering questions.

The simple version:

1. Take your documents 2. Break them into chunks (~500 words each) 3. Convert each chunk to a vector embedding (a list of numbers that capture meaning) 4. Store in a vector database (pgvector in Postgres works great) 5. When a user asks a question, find the most relevant chunks 6. Send those chunks + the question to Claude 7. Claude answers based on your actual documents, not its training data

Cost to run: ~$15/month for a small business knowledge base. Processing 1,000 questions per day costs about $2 in Claude API calls.

What we've built this for:

• ▸HR bot that answers policy questions (instead of employees emailing HR)
• ▸Product knowledge base for a distributor's sales team
• ▸Legal precedent lookup for a small law firm
• ▸Manufacturing SOP assistant on the factory floor

The tools: LangChain (Python) + pgvector + Claude. That's the entire stack.

Want us to build this for you? Most RAG systems take 2–3 weeks. Get a free quote.