Skip to content
AI Mastery
Lesson 8 · In the wild

Where RAG is used — and try it yourself

RAG powers most AI tools that seem to "know" specific documents: chatbots that answer from a company's help center, assistants that search internal wikis, research tools that cite sources, and customer support that pulls from product manuals. Below, a simulated RAG pipeline lets you watch Retrieve → Augment → Generate happen on a tiny example.

Scroll

Common real-world uses

  • Support chatbots that answer from a help center or product manual — with citations.
  • "Chat with your documents" tools for contracts, research papers, or wikis.
  • Internal knowledge assistants that search a company's private data.
  • Research and analysis tools that must show where each claim came from.

Try it: a simulated RAG pipeline

Pick a question below. Watch the pipeline embed it, light up the matching chunks in the mini knowledge base, paste them into the prompt, and generate a grounded answer. Everything here runs in your browser on a fixed example — there's no real AI model and nothing leaves your device. It's a teaching model of the real thing.

You've finished the course

You now understand RAG end to end: an LLM predicts words and can hallucinate; embeddings turn meaning into vectors; similarity finds the closest ones; semantic search retrieves relevant text; and RAG feeds that text to the model to generate grounded answers. Ready to go deeper or build one? The links below continue the journey.

InteractiveA fixed, offline demo of the RAG pipeline.
Explore the full question bank →
what is RAG
What is RAG? Retrieve, Augment, Generate
RAG vs fine-tuning
RAG vs fine-tuning
vector embeddings explained
What are vectors and embeddings?