Private AI for technical documentation

Documentation is usually the first thing a team lets slip and the first thing a new hire wishes existed. An LLM that reads a codebase and drafts or updates docs from it can close a lot of that gap, but reading a codebase means having access to it, and for proprietary code, that access shouldn't require sending the source to someone else's servers.

What this actually saves

Writing documentation from scratch is slow because it requires reading code that's often undocumented in the first place, then writing a clear explanation of what it does and why. A model can draft that first pass: reading a function, module, or service and writing a description of its behavior, its inputs and outputs, and any non-obvious constraints it can infer from the code itself. It's also useful for the more tedious maintenance problem of stale docs: comparing an existing doc against the current code and flagging where they've diverged, which is a task most teams skip because nobody wants to manually diff prose against source.

None of this removes the need for a human editor. A model reading code can describe what the code does, but it can't reliably explain why a decision was made or what alternative was rejected and why, the kind of context that usually lives in someone's head or an old pull request discussion. Docs generated this way work best as a draft that an engineer who knows the system corrects and approves, not as a final artifact.

Why proprietary code is the wrong thing to hand a cloud tool

The parts of a codebase most worth documenting well tend to be the parts a company has the least interest in exposing: core business logic, architecture decisions that encode competitive advantage, authentication and authorization code where a documentation error could also reveal a security gap. Running a hosted coding assistant against that code for documentation purposes means the same source that goes into a code review tool goes here too, under the same third-party terms of service and the same uncertainty about training and retention. See our post on private AI for code review for the same argument applied to reviewing diffs rather than documenting modules; the exposure is the same shape.

A self-hosted model reads the same code without transmitting it anywhere outside the organization's own infrastructure. That matters more for documentation than it might first seem, because a documentation pass by design touches every file in a codebase over time, not just the files changed in a given week, so the exposure compounds if it's running against a hosted service continuously.

How teams set this up

The common pattern is a coding model with retrieval over the repository: rather than loading the whole codebase into context at once, the system pulls in the specific file being documented plus related files (callers, dependencies, existing docs) as needed, the same retrieval approach covered in our guide to on-premise RAG. Wiring this into a CI step that runs on merged pull requests keeps docs from drifting far behind the code, flagging a docstring that no longer matches a changed function signature, for instance, without requiring anyone to remember to update it manually.

For teams already running a self-hosted assistant for coding day to day, our comparison of self-hosted coding assistants against Copilot covers the broader infrastructure question; documentation generation is typically an added task on the same running model rather than a separate deployment. A DGX Spark node's 128 GB of unified memory holds a capable coding model with enough context budget to read a file and its immediate dependencies in one pass, and at $0.79/hour for a single-GPU node, running this as a periodic CI job costs a fraction of what continuous usage would.

faq