Ground Truth
Known correct answers, expected outputs, or validated reference data used to evaluate AI system accuracy and performance.
Overview
Ground truth represents the "correct" or expected answer that serves as a reference point for evaluation. In LLM testing, ground truth helps validate factual accuracy, expected behaviors, and response quality against known standards.
Types of Ground Truth
Factual ground truth consists of verifiable facts that have definitive correct answers. This includes historical dates and events, mathematical calculations, scientific facts and formulas, geographic information, and objective product specifications. These facts serve as clear benchmarks because there's little ambiguity about correctness.
Behavioral ground truth defines expected system behaviors rather than specific factual answers. This includes appropriate refusals to harmful requests, required compliance with policies, mandatory disclaimers or warnings, format requirements, and tool usage patterns. Behavioral ground truth focuses on how the system should act, not just what information it should provide.
Quality ground truth establishes standards for response quality when multiple valid answers exist. This encompasses tone and style guidelines, completeness requirements, citation and source standards, and language and terminology expectations. Quality ground truth is more subjective than factual ground truth but still provides important evaluation criteria.
Using Ground Truth in Testing
Exact match validation works best for outputs with single correct answers, such as mathematical problems or specific factual questions. When responses may vary in wording but should convey the same meaning, semantic similarity evaluation compares the intent and content rather than exact phrasing. Behavioral validation checks if responses match expected behavior patterns, ensuring the system acts appropriately even when exact output varies.
Creating Ground Truth Datasets
Manual curation involves carefully creating ground truth examples through systematic review of your use cases. This requires identifying representative scenarios, determining correct answers or behaviors, and documenting the reasoning behind each ground truth label. Expert validation brings in domain specialists to create ground truth for specialized domains where accuracy is critical or requires deep expertise.
For high-stakes applications, have domain experts review and verify ground truth to ensure accuracy. Cross-reference information from multiple authoritative sources to validate correctness. Document where each ground truth example comes from to maintain transparency and enable future updates.
Ground Truth in Different Testing Scenarios
Knowledge testing uses ground truth to verify factual accuracy, comparing AI responses against verified information. Safety testing relies on ground truth examples of harmful versus acceptable content to train and evaluate safety metrics. Multi-turn testing with Penelope requires ground truth about appropriate conversation flows and goal achievement patterns, defining what successful completion looks like.
Challenges with Ground Truth
Many questions have multiple valid answers that are all equally correct. A question about travel recommendations might have dozens of good responses, none definitively "better" than the others. This makes exact ground truth impossible and requires more flexible evaluation approaches.
Some aspects of quality don't have objective ground truth. Whether a response is "friendly enough" or "sufficiently detailed" involves subjective judgment that varies between reviewers. In these cases, multiple human evaluators can help establish consensus ground truth.
Ground truth can change over time as facts evolve. Company policies update, product specifications change, scientific understanding improves, and current events make previous information obsolete. Maintaining ground truth requires regular reviews and updates to stay accurate.
Best Practices
For creating and maintaining ground truth, involve domain experts to verify critical ground truth in specialized areas. Cross-reference information from multiple authoritative sources before establishing it as ground truth. Document sources carefully, tracking where each ground truth example comes from. Implement version control to update ground truth as facts change over time. Mark confidence levels to indicate certainty, flagging areas where ground truth might be ambiguous or disputed.
When using ground truth for evaluation, choose appropriate metrics that match your ground truth type—exact match for factual questions, semantic similarity for paraphraseable content, and rubrics for quality assessment. Allow flexibility for valid variations in wording when multiple phrasings convey the same correct meaning. Consider context, as what qualifies as a correct answer may depend on user intent or conversation state. Schedule regular reviews and updates to keep ground truth aligned with current information.
For validation and quality assurance, conduct periodic human review to validate that automated checks against ground truth work correctly. Test ground truth with unusual formulations to ensure it's robust to different ways of expressing the same query. Check for contradictions within your ground truth dataset where one example might conflict with another. Ensure coverage across all important scenarios your system should handle, not just easy common cases.