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Insights

Practical notes for teams building with human data.

Clear explanations of the operating, quality and procurement questions behind successful AI training data programmes.

Evaluation
Evaluation · 7 min read

How human evaluation improves AI models

A practical look at rubrics, calibration and the signals model teams can actually use.

In this guide

  • Choosing useful evaluation dimensions
  • Building calibration examples
  • Turning human feedback into model signals
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Quality
Quality · 7 min read

Quality control for AI data annotation

Why good guidelines are only the beginning—and how review systems prevent drift.

In this guide

  • Preventing instruction drift
  • Sampling and layered review
  • Using performance data constructively
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Procurement
Procurement · 6 min read

Selecting an AI training data partner

The questions that reveal how a provider manages people, data, risk and accountability.

In this guide

  • Assessing contributor operations
  • Reviewing security claims
  • Structuring a useful pilot
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Need a procurement resource?

Use our capability overview in your internal review.

View capability statement