Why does AI keep making the same mistake?
Because the correction went into the conversation, not into anything the next conversation reads. Nothing in the model changes when you correct it, and then the chat ends. To make a correction stick, it has to be written somewhere durable, carry the situation it applies to, and be handed back on the next task where that situation comes up.
Correcting the answer is not the same as correcting the system
When you tell the AI it got something wrong, two useful things happen and one does not. The current answer improves. Your reasoning is now in the conversation, so the rest of that session is better. What does not happen is any change to what the next session knows.
That is why the fifth time you explain that a particular customer is on legacy pricing feels worse than the first. The AI is not being stubborn. Nothing ever wrote it down.
Five real approaches, and what each one is good for
Add a line to the instructions file
The standard answer, and a good one. Every correction becomes a rule the AI reads at the start of the session.
Keep a lessons document
A running list of things it got wrong. Works well because a person curates it, which is also the reason it stops being maintained.
Re-prompt harder
Tighten the instruction until the mistake stops. Effective for one task, and it teaches the prompt rather than the organization.
Fine-tune a model
Genuinely changes the model's behavior. It is the right tool for style and format at scale, and a heavy one for a rule that changes next quarter.
A memory system that saves facts
Some memory systems are very good at saving facts and preferences, without you managing a file. Organizational learning adds another question: when should this correction apply, who should receive it, and when should it stay personal?
Where a rules file is the whole answer
For one person with a stable set of preferences, appending corrections to an instructions file is close to perfect. It is visible, it is versioned if you keep it in a repository, and loading it costs almost nothing.
It works because every rule in the file is a rule you want applied every time. As long as that stays true, there is nothing to improve.
Four ways a growing pile of corrections turns on you
| What you notice | What is actually happening |
|---|---|
| Answers get worse as the file gets longer | Every rule is loaded for every task, so the three that mattered are buried among forty that did not apply. |
| Two rules contradict each other | You corrected the same thing twice, months apart. Both lines are still there, and nothing marks which one is current. |
| A fix for one customer is applied to all of them | The correction was true in a specific situation. Written as a flat rule, it lost the condition that made it true. |
| Nobody knows who may change a rule | A file has no notion of authority. The person who knows the real policy and the person who guessed have the same edit access. |
Work, correction, durable lesson, better later work
While you are working, your AI can write the corrected version to HexaHQ as knowledge, carrying the situation it applies to. You review what it saved and choose who it reaches. The correction happens where you already were, in the middle of the task, which is the only moment you actually know what the rule should say.
On a later task, the corrections that apply to that task come back with it. Not the whole pile. That is the difference between a store of corrections and a set of rules that arrive when they are relevant.
A correction does not become a universal rule on its own, and it should not. HexaHQ keeps those decisions with the durable context: scope, authority, and applicability are part of what gets saved. HexaHQ does not decide by itself that something you did twice is now company process either. It can save the durable piece and propose it; a person reviews it and decides who it is for.
A correction is not automatically everyone's rule
A correction can stay yours, reach a named group, reach the whole organization, or go to one external collaborator. A person's AI can use the corrections that person is meant to have. So the pricing exception you were told in confidence does not turn up in a teammate's draft.
A misfiled conversation, and the expansion task it becomes next time
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The AI classifies it wrong
A support conversation in Intercom mentions adding seats. The AI files it as a billing question.
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The person corrects it naturally
"When a customer on an annual plan asks about seat count, that is an expansion signal and it goes to their account executive." The AI saves the rule with that condition attached, and the person checks it.
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A later task, a different conversation
The next week a similar message arrives. Because the customer is on an annual plan, the rule applies and comes with the task.
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Real work, not just a better label
The AI drafts the expansion note and creates the follow-up task in Salesforce for the account executive, under their own login, once it is approved.
Questions people ask next
How can I correct ChatGPT once and have it remember?
Save the correction outside the chat, with the situation it applies to, and make sure the next relevant task receives it. That is the whole mechanism. A model-native memory feature covers some of it for you personally, inside one product.
How do I teach AI my company's rules over time?
By correcting it during real work rather than running a documentation project. Each correction your AI saves to HexaHQ is reviewed by you and then available to the next task and the people you shared it with.
Does every correction become a rule for everyone?
No. That would be the wrong behavior. A correction carries scope and an audience, and a person decides whether it stays personal or reaches a team or the organization.
Do I have to write documentation for this to work?
No. The point is that the durable version is written as a by-product of the work. Your part is reviewing what was saved, which is much less effort than authoring it.
Is this fine-tuning?
No. Nothing about the model changes. The corrections live outside it and are supplied with the task, which is why they can be read, edited, versioned, and scoped to one team.
What if a correction turns out to be wrong?
Change it. Knowledge in HexaHQ has a version history, so you can see what it said before and go back to it.
Related pages
Correct it once. Make the correction stick.
Fix it in the middle of the work and let the next task start from the fix. Free to start.