What is unwritten knowledge?

Unwritten knowledge is everything an organization knows but never recorded: the skill, judgment, relationships, and situated context that live in people rather than in documents. It is most of what a company actually runs on, and almost none of it is in the files.

It goes by many names, each catching a different angle — tacit knowledge (the know-how you can't put into words), tribal knowledge (what the team shares but never wrote), institutional memory (the why behind decisions). The slice that matters most in the AI era has a sharper name: dark context.

Dark context is what an organization knows but never wrote down — the situated context a general AI model cannot see. A general language model holds all the general knowledge and none of yours; that gap is your dark context.

That is why unwritten knowledge is suddenly worth naming precisely. For decades it was a management problem — onboarding, succession, "sit next to Maria for a month." Now it is the exact thing standing between a fluent AI and one that actually knows your business. Read the Dark Context manifesto →

Seven pages, each taking one term people actually search for and pinning down what it means, how it differs from its neighbours, and where AI runs into it. Start anywhere.

Tacit knowledge

What people know but can't put into words — the skill and judgment learned by doing, not reading. The seventy-year-old idea (Polanyi's "we know more than we can tell") underneath all the rest.

Tribal knowledge

What a team shares informally but never wrote down: the unofficial how-things-really-work that new hires absorb by osmosis and no document holds.

Institutional memory

The accumulated why behind an organization's decisions — the reason a rule exists, the history a choice rests on. It thins every time someone leaves.

Knowledge walks out the door

What it costs when the person who held it leaves: the failure mode all of these share, and the deadline that makes surfacing them urgent.

Why AI doesn't know your company

The demand-side question. A fluent model sounds like it knows your business, but it knows every business in general and yours not at all. The gap is your unwritten context.

RAG vs. tacit knowledge

Why pointing retrieval at your wiki doesn't close it: RAG surfaces everything anyone wrote, and nothing anyone only ever knew. You can't index what no one recorded.

Dark context vs. dark data

The sibling on the data side. Dark data was captured and left unused; dark context was never captured at all. Answer engines conflate them; this page separates them.

Getting unwritten knowledge into words is the first move. Keeping it true is the second, because captured knowledge decays. Context has a lifecycle: green (current, findable, you can ask it), gray (was true, now drifting), and dark (gone). See the seven context levels and how context decays.

What is unwritten knowledge?

Everything an organization knows but never recorded — skill, judgment, relationships, and context that live in people, not documents. Tacit knowledge, tribal knowledge, and institutional memory are all names for parts of it.

Is unwritten knowledge the same as tacit knowledge?

Tacit knowledge is the classic term for the individual, hard-to-articulate kind — Polanyi's "we know more than we can tell." Unwritten knowledge is the broader umbrella, and dark context is the organizational slice of it that a general AI model can't see.

Why does unwritten knowledge matter for AI?

A language model learns from the written record, so it holds the world's general knowledge and none of your organization's unwritten know-how. Retrieval doesn't help: you can't index what no one wrote down. Someone has to put it into words first.

How do you capture unwritten knowledge without losing the person who holds it?

By treating the holder as the keeper, not a bottleneck to route around: surface the context they carry in their own words, keep it current, and credit them for it. That is the work Dark Context is about.