Retrieval-Augmented Generation (RAG)

LLMs are trained on general internet data up to a cutoff date. They know nothing about:

Your specific products

Your business processes

Your customer history

Any document you haven't shown them

For many business applications, this is a serious limitation. You need the AI to work with your specific knowledge, not just general knowledge.

RAG solves this by giving the AI real-time access to a knowledge base.

When you ask a question:

1. A retrieval system searches your knowledge base for relevant content

2. That content is added to the AI's context window

3. The AI generates a response using both its training knowledge and the retrieved content

The AI can now answer questions about your specific documents, products, or data — even though it was never trained on them.

You upload your product catalog (200 products, 50 pages) to a RAG system. A customer asks: "Do you have a scanner that works with Amazon products?"

Without RAG: The AI either makes up an answer or says it doesn't know.

With RAG: The system searches the catalog, retrieves the THOWBA Product Scanner description, and the AI answers with accurate product information.

Many tools you already use implement RAG:

**Perplexity** — retrieves from the web before answering

**Notion AI** — retrieves from your workspace documents

**ChatGPT with file upload** — retrieves from uploaded files

**Claude with document upload** — same

If you've ever uploaded a document to an AI and asked questions about it, you've used RAG.

For developers: vector databases (Pinecone, Weaviate, Supabase pgvector) + embedding models + LLM API. This is a meaningful technical project.

For non-developers: tools like Relevance AI, Botpress, and Voiceflow let you build RAG-powered chatbots without code. Upload your documents, connect to an LLM, deploy.