I’ve spent the past six months experimenting with the creation of organizational memory.
That’s sort of a pompous term for good end-user documentation produced at scale. But it helps me separate the age-old, mundane task of writing documentation from the new challenge of keeping overconfident LLMs from accelerating the human game of telephone.
Context makes LLMs work better. Thank you, Captain Obvious.
But how do you start building context?
Do I vibe-code an agent that extracts statistics from a table? Extract formulas from repos? Have an agent read through Confluence? Maybe Jira is the right place to start?
A few weeks ago, I think I figured it out: I need context that is so good that a user can’t fail to see the difference between an LLM querying data and an LLM querying data WITH MY CONTEXT.
So where do I start?
Not with an agent. With me.
I’m the subject-matter expert at Disney in transforming playback telemetry emitted by the Disney+ streaming service into how our subscribers and viewers experience our content. So I wrote a little skill called /query-and-context.
There’s a lot in that skill that’s specific to how I started building organizational memory, but here’s the bit of the prompt I think is interesting:
Identify what’s missing. After writing the query, note any context that would have helped you write it faster or better. Present these as proposed evidence items.
Be obnoxious about context. The whole point of this skill is to extract domain knowledge from experts while they investigate data. If the user says something of value—a business rule, a “well actually,” a correction, a clarification—but doesn’t take the time to refine it into submittable context, push back. Don’t refuse to proceed, but be disruptive: “That’s useful—can we capture that before moving on?” or “You just told me something the canon doesn’t know. Can I draft evidence for that?” Treat every offhand expert comment as a potential context contribution. Be a context magnet, not a polite observer.
Then I stopped working on the organizational-memory platform.
Instead, I dove back into the business. Retiring an old telemetry platform (which turned into a piracy thing). Deprecating an attribute (that’s another post). Trying to figure out how ad blockers appear in playback telemetry. Mitigating the inevitable anomalies. Regular-old new-data-product UAT and lifecycle work.
Except now I did the technical part of my job through /query-and-context.
And oh my god IT WAS SO FUCKING ANNOYING.
But it also felt like six months of random questions asked over IM concentrated into five minutes.
I like to think I’m obsessive about documentation. But it’s too easy to put it off for a day or a week and then forget about it. And there’s very little immediate reward for doing it.
With humans, I can’t see the impact of documenting a known issue or disambiguating two confusing columns.
With an LLM, I can see the difference immediately. That obnoxious prompt made the value of capturing context visible—and made the disruption worth it.
If I can figure out how to get from one user (me), to two users (me and a colleague), to twenty (my engineers), to a hundred (some additional domains), to a thousand (an entire alliance), I can do more than see that impact.
I can measure it.