How to Take Care of Your AI Context (So It Stays Accurate Over Time)
Most people set up their AI context once. Write a short bio, list a few preferences, paste it...
Most people set up their AI context once. Write a short bio, list a few preferences, paste it into a custom instructions box, and move on. Six months later the AI still thinks you're at your old job, working on a project you shipped in the spring, reporting to someone who left.
The context didn't break. It just never got looked after.
Personal context isn't a thing you write — it's a thing you maintain. And almost everyone only ever does the first step.
What does it mean to take care of your AI context?
It means treating your context as a living record, not a document you finish. The information an AI uses to understand you — your role, your projects, your priorities, how you work — changes constantly. Taking care of it is the ongoing work of keeping that record accurate as your life moves.
This is the part the "give the AI more context" advice skips. Everyone tells you to add context. Almost nobody tells you that context has a shelf life, and that the value comes from upkeep, not the initial dump.
The discipline of designing what a model sees has a name in the developer world — context engineering. Anthropic and LangChain both write about it as a full practice with distinct stages, not a one-shot prompt. The same logic applies to your personal context, scaled down to one person: it's something you manage over time, not something you set and forget.
Why does context go stale if you don't maintain it?
Because the facts it's built from keep changing, and a static document doesn't. The moment you write "I'm working on X," you've created a fact with an expiry date. You finish X. You start Y. The document still says X. The AI confidently builds its answers on something that stopped being true weeks ago.
Stale context is worse than thin context. A model with no information about your current project gives you a generic answer — unhelpful, but you know to correct it. A model working from wrong context gives you a confident, specific answer aimed at the wrong target. You trust it more and it's further off.
This is the long-horizon cousin of context rot — where an AI's grip on the conversation degrades inside a single long session. Staleness is the same decay across months instead of minutes: the information is technically present, but it no longer reflects reality, so leaning on it makes things worse.
What are the stages of looking after your context?
There are four, and they form a loop you cycle through continuously rather than a checklist you complete once.
Generate. Create the context in the first place — pull together who you are, what you do, and what you're working on. Done well, this comes from your real sources rather than memory. (Full method: how to build personal context.)
Curate. Decide what actually belongs. Not everything is worth keeping — good context is the right information, not the most. Trim trivia, sharpen what matters, keep it relevant to the tasks you actually do.
Keep current. Refresh facts as they change. New role, new project, new priority — the record updates to match. This is the stage that separates context that's useful in June from context that was useful in January.
Retire. Remove what's no longer true. The finished project, the old preference, the company you left. Retiring stale facts is as important as adding new ones — and it's the step almost nobody does, because deleting feels like losing information rather than improving accuracy.
LangChain frames the agent version of this as write, select, compress, and isolate — different vocabulary, same underlying idea: context is actively managed across its life, not written once and trusted forever.
Which parts does almost everyone skip?
Everything after "generate." People consciously do the first step — they write the bio, they fill the box — and then treat the job as done. Curating, keeping current, and retiring all happen by accident if they happen at all.
The reason is simple: the first step is a one-time burst of effort with a visible payoff. The other three are quiet, recurring chores with no obvious trigger. Nothing reminds you that your context went stale. The AI doesn't flag it. You only notice when an answer is confidently wrong about something that changed months ago — and by then the drift is everywhere.
This is also why platform memory doesn't rescue you. Tools like ChatGPT and Claude build memory from your conversations, but editing it is mostly limited to deleting entries — there's no clean way to revise a fact in place. (Editing what AI remembers covers how thin that control actually is.) The upkeep stages stay manual, so they stay undone.
How do you keep your context current without it becoming a chore?
You stop maintaining it by hand and connect it to sources that maintain themselves. This is the only version of context upkeep that survives contact with a busy week.
The problem with a hand-written context file is structural, not motivational. It's a snapshot the day you write it, and it starts drifting immediately. Keeping it current means remembering to edit a document that nothing prompts you to open — so you won't, and it'll rot. The discipline isn't the answer. Removing the need for discipline is.
The fix is to build context from sources that already change when your work changes. Your LinkedIn updates when your role does. Your Notion and email move as your projects move. Your GitHub reflects what you're actually shipping. If your context is extracted from those — rather than typed from memory — then keeping it current isn't a task you do. It's a side effect of living your work.
That's the model Unabyss is built on. Connect your real sources once, and your context is generated, structured, and kept current automatically — the four stages happen without you running them by hand. It's portable too: the same maintained context loads into every AI tool you use, through MCP, so you're not curating four separate copies in four separate apps. (For how that compares to letting each platform manage its own memory, see Unabyss vs. built-in memory.)
Taking care of your context is real work. The trick is to set it up so the work takes care of itself.
Build context that maintains itself →
