Should employee corrections train the next model?

A “save correction” button gives you material, and it does not explain what the model should learn from it. If an employee added a new fact, for example a changed delivery date, that information belongs in the knowledge base, because a model trained on such an example will remember a date that will change again in a month.

One correction, different reasons

Say the AI has prepared a message with a meeting time taken from the calendar. The employee changes the time because they have just agreed a new one by phone. If we keep only the first and last versions of the text, it looks like a model error. In reality the model never received the new arrangement.

Another correction shortens a several-sentence greeting in line with the shared communication rules. Such a case may show a repeated behaviour the company wants to change. The reviewer needs to understand the difference before accepting both entries as patterns for the next version.

Collecting corrections without their reasons risks training on changes that cannot be reproduced from the information available. It is also worth keeping the correct answers. A list of errors alone does not show what the current model already does well and what a new version should not make worse.

Who decides which user corrections shape the next AI model

The employee knows the case, and their preference is not always a rule for the whole company. The process owner sets which corrections should apply more widely. When assessments differ, the person responsible for that type of work has to decide. The system can make that conversation easier by showing the specific differences and the sources.

Hugging Face describes dataset cards as documentation of a dataset's content and the context of its use. In a company workflow, a similar description lets you explain where approved corrections come from and what they represent. Being saved on a list does not automatically make them a correct pattern.

Not every batch of feedback has to end in training. If most of it concerns outdated information, the connection to the source may need fixing. If one editorial rule keeps coming back, change the instructions first. Fine-tuning, further training of an existing model, is an option for behaviours that a simpler change does not fix well enough.

A new version needs its own check

Before replacing the current model, the team compares both versions on cases outside the approved corrections used for adaptation. It judges whether the improvement shows in new answers and whether information that was handled correctly before has disappeared. The history of corrections helps choose where to look, and it does not replace such a comparison.

The buyer should also know what happens to a piece of feedback after it is saved. Does someone review it, or does it stay only in a statistic? Who agrees to the material being used, and who accepts the new version? These arrangements decide whether the error-report button leads to a useful change.

Describe a correction that employees save again and again, and how it is used afterwards. In a conversation with Syntalith we will settle where an expert's assessment is needed and what scope of linking feedback to the app's development makes sense. Information about working together is on the pricing page.

What fine-tuning on corrections costs and what it saves

Where to start

In the free process review we choose one process and 20 to 100 cases the team has already handled. We compare the simpler change with the adapted model on cases the model has not seen, and within 2 working days you get a written summary with a price. We adapt models as part of private LLMs, and build the app as an AI app.