Factuality

Full definition

Factuality is measured with benchmarks such as TruthfulQA, FActScore, and SimpleQA, plus retrieval-grounded checks where each claim must be supported by a cited source. The opposite of factuality is hallucination. In the 2023 FActScore study, only 58% of the atomic facts in ChatGPT-generated biographies were supported by a reliable knowledge source — making factuality a first-class governance metric.

Why it matters

For regulated industries, factuality is the gating constraint on LLM deployment. A model that is 5% wrong on medical or legal facts is unshippable regardless of how fluent it is. Buyers should demand factuality numbers, not just leaderboard scores.

Example

A bank's internal copilot is benchmarked on a 2,000-question regulatory-knowledge dataset; factuality below 95% blocks promotion to production until RAG grounding is added.

Next steps

• Get a free workflow map →We map one of your workflows and give you a written verdict: software, a process change or AI. No obligation.
• AI consulting cost guide →Hourly rates and project budgets by firm tier, from independents to the Big Four.

Related terms

• Hallucination
  An AI hallucination is when a language or generative model produces content that is plausible-sounding but factually incorrect, fabricated, or unfaithful to its sources — one of the biggest failure modes of LLMs in regulated and high-stakes domains.
• Retrieval-Augmented Generation (RAG)
  Retrieval-Augmented Generation (RAG) is a technique where a language model retrieves relevant documents from an external knowledge base before generating a response, which reduces hallucination and enables citation of sources.
• Model Evaluation
  Model evaluation is the systematic measurement of an AI model's performance, safety, and behavior across representative tasks — using fixed benchmarks, golden datasets, human ratings, and LLM-as-judge methods — both before and after deployment.
• AI Guardrails
  AI guardrails are the layered controls — input filters, output classifiers, policy engines, schema validation, and structured generation — that constrain an AI system to safe, on-policy, and on-task behavior.

More governance & risk terms

• Jailbreak
  A jailbreak is an adversarial prompt or technique that bypasses an AI model's safety guardrails to elicit content or behaviors the model was trained to refuse — for example, hazardous instructions, restricted personal data, or off-policy assertions.
• Prompt Injection
  Prompt injection is an attack in which adversarial text — placed directly in user input or hidden inside content the model retrieves — overrides developer system instructions and hijacks the model's behavior, exfiltrating data or causing unauthorized actions.
• AI Red-Teaming
  AI red-teaming is the practice of systematically probing an AI system — using adversarial prompts, jailbreak techniques, and edge-case inputs — to surface harmful, unsafe, biased, or policy-violating behaviors before and after deployment.