Factual Accuracy Rate
How accurately does AI describe your brand? Not how positively, not how often. Accurately. This is the metric most teams skip, and the one that causes the most damage when ignored.
The short version
- Sentiment is the wrong metric for AI output. LLMs are trained to sound balanced. Sentiment scores on AI responses are almost always neutral, whether the description is accurate or completely wrong.
- Hallucination is not random. AI fills gaps with plausible-sounding fabrications. The less coverage you have, the more likely inaccuracy becomes.
- Three types of inaccuracy: invented claims, outdated information, and attribution errors. Each has a different cause and a different fix.
- The audit is straightforward. Run prompts, compare against ground truth, classify every inaccuracy, calculate your rate.
Two chapters worth reading first
Factual accuracy is a perception metric because inaccurate descriptions directly shape how buyers experience your brand through AI. Understanding where those inaccuracies come from, and how to collect the data to find them, makes the audit significantly more useful.
Ch. 7 – AI Visibility Channels
Accuracy audits should be run with browsing disabled to test training data recall. Chapter 7 explains why this matters and how training data and grounded search produce different kinds of inaccuracy.
Ch. 10 – Data Gathering Methods
Running a reliable accuracy audit means collecting responses at scale across multiple models. Chapter 10 covers how to do this without introducing bias that makes your results untrustworthy.
Why sentiment is the wrong metric
Neutral is not the same as accurate
Large language models are trained to produce balanced, informative responses. Applying sentiment analysis to AI output almost always returns neutral, regardless of whether what the model says is true. This makes sentiment a useless signal for brand monitoring in AI.
Tone
Whether the language is warm, cold, or critical. In AI output this is almost always neutral, not because the model thinks well of you, but because it is designed to be balanced.
Truth
Whether the claims the model makes about your brand are actually correct. This is the signal that affects buyers.
A brand can receive consistently neutral sentiment scores while the model simultaneously states it was acquired by a competitor, discontinued its flagship product, or serves a customer segment it has never targeted. Sentiment tracking catches none of that.
Why inaccuracy happens
Three types, three different fixes
AI does not hallucinate randomly. It fills gaps in its training data with things that sound plausible based on patterns it has seen elsewhere. The less coverage you have, the more gaps there are to fill. Understanding which type of inaccuracy you are dealing with determines what you do about it.
Invented claims
The model generates details with no basis in your actual content. Products you do not make, partnerships you have not announced, certifications you do not hold. Happens most often when training data coverage is thin and the model is filling in what it expects a brand like yours to have.
Outdated information
The model accurately recalls something that was true during training but is no longer accurate. Leadership changes, product pivots, pricing updates. The model has no way to know things have changed unless updated sources outweigh the historical ones.
Attribution errors
The model correctly recalls a fact but assigns it to the wrong entity. A competitor’s acquisition attributed to you, your feature attributed to a competitor. These often trace back to a single piece of content where two brands were mentioned together in a confusing context.
How one bad source created a false claim across multiple models
InLinks, Waikay’s sister software, discovered through AI monitoring that multiple models were consistently stating it had been acquired by SEMrush. The claim was completely wrong but was generated with the same confidence as accurate information.
The inaccuracy was traced to the transcript of a YouTube video in French where SEMrush and InLinks were discussed together in a context the model had misread as an acquisition announcement. That single source was enough to create a consistent false claim across multiple models.
Without monitoring, this would have been invisible, appearing in buyer-facing AI responses for months or years. With monitoring, it was identified, traced, and addressed.
You do not need widespread bad coverage to create a persistent inaccuracy. One misread source in the training data is enough. The only way to know it is there is to test for it systematically.
Running the audit
Define your ground truth
Write down the factual claims you will check against: founding date, leadership team, product capabilities, pricing model, customer segments, partnerships. This is your scorecard. Without it, you have nothing to compare responses against.
Run accuracy-focused prompts with browsing off
Turn off live search to test what the model has actually learned from training data. Prompts like “Describe [Brand] and its main products” and “Who founded [Brand] and when?” force the model to draw on trained knowledge directly. Run multiple times across at least two models.
Log every inaccuracy and classify it
Compare each response against your ground truth. Log every discrepancy. Classify each one as invented, outdated, or misattributed. The classification matters because it determines the fix.
What to log
The exact claim as stated. The correct information. The inaccuracy type. Which models produced it and how consistently. Any hypothesis about the source.
Calculate your accuracy rate
A verifiable claim is any specific, checkable statement: a date, a name, a product capability, a partnership. Not “Brand X is a workflow tool” but “Brand X integrates with Salesforce.” Underline every checkable claim in each response, verify each one, and divide correct claims by total claims.
Example
A response contains 8 verifiable claims. 6 are accurate, 1 is outdated, 1 is invented. Factual Accuracy Rate: 75%. Track across 10 runs to get a stable baseline.
Monitor on a regular cadence
Run your accuracy audit quarterly at minimum. Pay particular attention after major model updates. These are the moments when previously corrected inaccuracies can reappear as models are retrained.
FAR = Verifiable claims that are correct / All verifiable claims across all runs
Track this number over time, not just as a one-off audit. Changes in your score, up or down, are the signal worth watching.
Fixing what you find
Invented claims
Publish clear, authoritative content establishing the accurate version on your own domain and on high-authority third-party platforms. If the invented claim points to a capability you do not have, publish content that clearly establishes what you actually offer instead. The goal is to give the model better, more prominent sources to learn from.
Outdated information
Update your own content first: About pages, leadership bios, product pages. Then get those updates reflected on high-authority third-party sources. The model will keep recalling the outdated version until newer sources outweigh the historical ones in its training data.
Attribution errors
Trace the error to its source if you can. Attribution errors often originate from a specific piece of content the model misread. Where the source can be corrected or removed, pursue that. In parallel, publish content that clearly establishes the accurate attribution across authoritative platforms.
Waikay tracks model outputs against a defined ground truth, classifies inaccuracies by type, and monitors your accuracy rate over time. New errors are surfaced as they emerge rather than only being caught when you think to run a manual audit.
