Learning Center
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What to look for in a data collection partner
Four criteria separate data collection partners that improve model performance from those that delay it. Here's the evaluation framework to use before you sign.
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Buy an existing speech dataset or commission a custom one?
Choosing a speech dataset? Compare the hidden costs of off-the-shelf corpora against commissioning custom data using this practical decision framework.
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When a multimodal project calls for a specialized partner
Discover the three failure modes that stall multimodal projects and the diagnostic that tells you when a specialized data partner changes the outcome.
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How providers source genuine subject-matter experts
Learn the three criteria data providers use to source SMEs who can anchor AI model quality, beyond résumé credentials and job titles.
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How to write a brief that gets you the training data you need
Learn how to write a training data brief that controls annotation quality: task scope, legal provenance, IAA thresholds, and workflow configuration.
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How to build a red teaming dataset
Learn how to build a red teaming dataset with a reusable schema, three sourcing tiers, expert annotation, and a maintenance workflow that keeps your model's safety coverage…
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How to evaluate agent planning
Learn how to evaluate agent planning at the step level using a four-dimension rubric, human trace review, and a feedback loop that improves planning over time.
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What is data quality assurance for AI?
Data quality assurance for AI is a continuous workflow, not a preprocessing step. Learn the failure modes, quality dimensions, and remediation loops that matter.
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How to evaluate agent memory
Static benchmarks miss three of the four memory failure modes that break agents in production. Here's how to build an eval that catches all of them.
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How to handle non-determinism in agent evaluation
Temperature=0 doesn't make agents deterministic. Learn a tiered evaluation approach using multi-run baselines and human review to close the reliability gap.
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What is speech data collection?
Speech data collection is more than recording audio. Learn why collection conditions set the ceiling on model performance and how quality control works at scale.
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What is a multimodal dataset?
A multimodal dataset isn't just mixed file types in one place. Learn what alignment between modalities actually means and why it determines whether your AI learns anything real.
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Step-level vs. outcome-level evaluation: What's the difference?
Learn the difference between step-level and outcome-level evaluation, and why your choice determines if your model's reasoning is genuine or decorative.
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How to build an agent evaluation dataset
Learn how to build an agent evaluation dataset: pull production traces, write a scoring rubric, and calibrate automated judges against human ground truth.
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How to evaluate agent tool use
Learn how to evaluate agent tool use beyond uptime metrics: catch selection errors, schema mismatches, and execution failures before they reach users.
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How to build a labeling tool for legal argument and citation annotation
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How to build a labeling tool for video captioning and dense event description
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How to build a labeling tool for CSAM adjacent and sensitive content triage
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How to build a labeling tool for ASR hypothesis selection
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How to build a labeling tool for k-way ranked response collection
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How to build a labeling tool for TTS and voice cloning sample review
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How to build a labeling tool for industrial time series anomaly review with synced video
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How to build a labeling tool for multi model pre annotation review with ranking and LLM as judge
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How to build a labeling tool for multi page document routing and splitting