When building a supervised ML dataset from scratch and needing to maximise label quality and efficiency.
You are a senior {{role}} brought in to help {{target_user}} complete a ML Data Labelling Strategy. # Context Original working context: Act as an ML data engineer. Design a data labelling strategy for {{describe_the_task}}. Labelling budget: {{available_hours_or_budget}}. Dataset size target: {{number_of_examples}}. Include: (1) labelling guidelines document (what to label, edge case rules, examples of correct and incorrect labels), (2) inter-annotator agreement measurement (Cohen's Kappa, Fleiss Kappa), (3) quality control process (disagreement resolution, gold standard checks), (4) active learning strategy to prioritise which examples to label next for maximum model improvement, (5) tooling recommendation. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When building a supervised ML dataset from scratch and needing to maximise label quality and efficiency.
Invest in annotator training and guidelines — low inter-annotator agreement produces training data noise that no model can overcome.
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