How to Build a Private RAG From Your Own Docs

Most "AI knowledge base" products quietly mean the same thing: your documents get uploaded to someone else's servers, chunked, embedded with someone else's model, and stored in someone else's database. That's fine for a lot of use cases. It's not fine when the documents are your company's Jira boards, internal wikis, or a codebase under an NDA. A personal RAG knowledge base flips that — the pipeline runs against your accounts, with your keys, and the index belongs to you.

What Actually Goes Into a Personal RAG

RAG (retrieval-augmented generation) is a two-step trick: retrieve the most relevant chunks of your own content for a given question, then hand those chunks to a language model as context so it answers from your material instead of guessing from its training data. The "personal" or "private" part isn't a different algorithm — it's a different trust boundary. The retrieval index, the embeddings, and the raw source files all stay scoped to your account instead of being pooled with everyone else's.

Choosing What to Feed It

The useful starting set is usually smaller than people expect. Three sources cover most real questions:

• A GitHub repo — so questions about "how does X work" can be answered from the actual implementation, not a stale README.
• PDFs and screenshots — specs, design docs, architecture diagrams, anything that only exists as a file, not a webpage.
• Wiki or doc links — the living documentation your team already maintains, instead of duplicating it somewhere else.

Resist the urge to dump everything in at once. A RAG index answers better when it's scoped tightly to what you'll actually ask about — a repo plus its related specs beats a repo plus five years of unrelated meeting notes.

Chunking and Embeddings, Briefly

Under the hood, each source gets split into chunks — small enough that a handful of them fit in a model's context window, large enough that each chunk still makes sense on its own. Every chunk is converted into an embedding, a vector that captures its meaning, and stored in a vector database. When you ask a question, your question is embedded too, and the system finds the stored chunks whose vectors are closest to it — that's the "retrieval" in retrieval-augmented generation. None of this requires exotic infrastructure; a Postgres database with the pgvector extension handles it comfortably for a personal-scale knowledge base.

Keeping It Actually Private

Privacy here comes down to three concrete things, not a marketing claim: row-level security so your data is unreadable by other accounts even in a shared database, encryption for any credentials used to pull from your connected tools, and — critically — using your own model API keys rather than a shared pooled key, so your content is never mixed into someone else's request logs or used to improve a model you don't control.

Getting Started

You don't need a data pipeline team to try this. Connect a repo, drop in a couple of PDFs, add a wiki link — that's enough to see whether grounded answers actually save you time before you invest in anything bigger. That's the whole premise behind AI Work For Me's Data Sources: connect what you have, bring the model keys you already pay for, and the index is yours from the first file.