Improve Accuracy Over Time

A Nyckel prediction endpoint is not a static model. Every new labeled example makes the model smarter. Here is how to improve accuracy systematically.

Start small, iterate fast

You do not need a large dataset to start. Begin with 5–10 examples per label to get an initial model. Invoke it, review the predictions, and correct mistakes. Repeat.

The first few rounds of correction typically produce the largest accuracy gains.

Prioritize corrections over new data collection

Correcting an existing misprediction is often more valuable than adding a new example from scratch — because the corrected prediction represents a case the model is already getting wrong.

To correct a prediction, annotate the sample via the console or API:

POST https://www.nyckel.com/v1/functions/{functionId}/samples/{sampleId}/annotation
Content-Type: application/json

{ "labelName": "Technical" }

Focus corrections where confidence is lowest

Low-confidence predictions are where the model is most uncertain. Correcting these teaches the model the most. See Confidence scores and thresholds.

Add examples for underperforming labels

If one label consistently has lower accuracy than others, add more labeled examples for it. Look at the accuracy breakdown per label in the Nyckel console.

Avoid adding noise

Do not add examples that are mislabeled, ambiguous, or inconsistent. Noisy training data reduces accuracy. When in doubt, leave an example unlabeled rather than guessing.