Lion Cipher Lab
Build a private RAG app on your own machine
- AI Security
- Beginner
- ~60 min
- Free · no account required
Why it matters
Retrieval-Augmented Generation (RAG) is how you make a language model answer from YOUR documents instead of only its training data: you retrieve the most relevant chunks of your text and paste them into the prompt as context, so the model answers grounded in real, current, checkable material. It's the single most common pattern in real-world AI applications — support bots, internal search, document Q&A — and building a small one yourself demystifies the whole category. Doing it LOCALLY matters twice over: your documents never leave your machine (a genuine privacy win over pasting them into a…
What you'll learn
- what RAG is and why it grounds a model
- embedding text with a local model
- storing/searching vectors in a local database
- retrieving context and augmenting a prompt
- keeping documents private by running locally
Steps in this lab
- What RAG is, in one picture
- Run a local model (recap)
- Turn text into embeddings
- Store your chunks in a local vector database
- Retrieve, then augment the prompt
- See the limits: grounding reduces, doesn't guarantee
- Why building one changes how you use AI