Usually, by the time an AI trainer starts working with a model, it has already been through large-scale pre-training and carries an enormous amount of factual knowledge. The contributor's job isn't to supply that knowledge from scratch. What contributors actually do looks less like teaching facts and more like:

• Refining: Rewriting a model's response to the standard a professional in that field would actually produce

• Evaluating: Comparing multiple responses and judging which one would hold up under real scrutiny

• Creating: Writing realistic, difficult scenarios that test how a model handles genuine professional complexity in your field.

• Scoring: Applying a provided rubric to rate model performance across defined dimensions, with written justification.

• Red teaming: Deliberately probing a model to find where it fails, hallucinates, or produces something unsafe

These five types of project tasks — refining, evaluating, creating, scoring, red teaming — all share one thing: none of them is about feeding the model new information. So why are they built this way?

Research found that trying to teach an already-trained model new facts often backfires. The model absorbs new information far more slowly than what it’s already familiar with, and once it does pick up new facts this way, it measurably becomes more likely to hallucinate. 

A separate study found that a model fine-tuned on just 1,000 carefully chosen examples performed remarkably close to models trained on far larger datasets. What does this mean? Most of a model's knowledge comes from its earlier training, and a smaller amount of curated data is needed to shape how it communicates that knowledge.

Put these two findings together and the same picture emerges: by the time contributors work with a model, it already holds the knowledge it needs. Trying to push new facts into it at that point barely works and can even make it less reliable. So these tasks aren't built around adding knowledge. They're built around a different job: shaping how the model reasons, formats, and communicates what it already knows.

If you pictured this as pouring knowledge into a blank student, that's not quite the right process. What's really needed is someone who can catch a subtly wrong answer, judge whether a response holds up, and know a field well enough to spot the difference.