Ground Truth
The authoritative data source or label set used to evaluate model accuracy and monitor drift over time.
Ground truth is the authoritative set of correct answers or data used to judge accuracy. It anchors evaluation, training, and monitoring for models and rules.
Businesses use ground truth to score predictions in fraud, classification, search relevance, and QA on generated content. It also powers drift detection when performance changes.
In workflows, ground truth lives in labeled datasets, gold test cases, or system-of-record tables. Good ground truth reduces disagreement, speeds audits, and improves model updates.
Frequently Asked Questions
How do I create reliable ground truth?
Define clear labeling guidelines, use multiple annotators, measure agreement, and review edge cases. Keep labels tied to the business objective.
How large should my ground truth set be?
Enough to cover key classes and edge cases; size depends on task complexity. Start small, expand as you see error patterns.
How do I keep ground truth fresh?
Add new examples as data shifts, review mislabeled items, and retire outdated labels. Schedule periodic refreshes.
Can I use production data as ground truth?
Only after validation. Raw production data may include noise or biases. Use it to find candidates, then label carefully.
How do I measure label quality?
Inter-annotator agreement, spot checks, and correlation with downstream performance. Track error types tied to bad labels.
What about privacy when labeling?
Mask PII, use secure tools, and limit access. For sensitive data, keep labeling in-house or use vetted vendors.
How does ground truth support drift monitoring?
Compare live outputs to ground truth samples. Rising error rates or changed distributions flag drift that needs investigation.
Should ground truth include hard negatives?
Yes—include common confusions and edge cases. This sharpens evaluation and highlights weaknesses.
How do I version ground truth datasets?
Store datasets with IDs, timestamps, and schema versions. Track changes so evaluations are reproducible.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
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A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
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A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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