Data · Foundational · Beginner

Training Data

The examples a model learns from, and the single biggest determinant of what it can and cannot do.

What Training Data is

Training data defines the model's worldview. Gaps, imbalances and annotation conventions in that data show up directly as behaviour in the deployed system.

How it works

Data is collected, cleaned, de-duplicated, filtered for quality and safety, split for evaluation, and documented. Large model pretraining adds heavy filtering and de-contamination against benchmarks.

Why it matters

Debugging a model usually means debugging its data. Provenance and licensing of training data have also become legal and reputational questions, not just technical ones.

Common uses

→Corpus construction for foundation models
→Labelled datasets for supervised tasks
→Evaluation and holdout sets

Strengths

✓Improving data is often cheaper than a bigger model

Watch for

✓Bias in, bias out
✓Licensing and consent issues
✓Benchmark contamination inflates scores

Continue exploring

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