Factual validation is a technique that verifies AI-generated content against authoritative source documents before delivery. It compares claims in the output to known facts in your knowledge base, flagging or correcting discrepancies. For businesses, this prevents confidently wrong answers from reaching customers. Without it, AI systems can sound accurate while being factually incorrect.
Your AI support bot confidently tells a customer the return policy is 30 days.
It is actually 14 days. The customer returns a product on day 20.
Now you have an angry customer, a policy exception to process, and zero trust in your AI.
AI systems do not know when they are wrong. They sound equally confident whether accurate or fabricating.
QUALITY LAYER - Catches factual errors before they reach users.
Checking AI claims against what you actually know
Factual validation compares what the AI says against authoritative sources in your knowledge base. When the AI claims "refunds take 3-5 business days," validation searches for the actual policy and confirms or corrects before delivery.
The goal is not to prevent all AI errors. It is to catch the errors that matter. A slightly awkward phrasing is harmless. A wrong price, outdated policy, or fabricated procedure damages trust and creates real business problems.
The AI has no mechanism to distinguish verified knowledge from confident guessing. Validation provides that mechanism by grounding outputs in your actual documents.
Factual validation solves a universal problem: how do you ensure someone (or something) is giving accurate information, not just confident-sounding information? The same pattern appears anywhere claims must be verified before action.
Extract verifiable claims from output. Search authoritative sources for evidence. Compare and flag discrepancies. Either correct, escalate, or block based on validation result.
The AI generated a response about return policies. 3 of 5 claims are wrong. Select a validation level to see which errors get caught.
Three approaches to validating AI outputs
Extract then verify
Parse the AI output to identify specific claims (numbers, dates, policies, names). For each claim, search your knowledge base. If no supporting evidence exists, flag or correct.
Compare directly to sources
Retrieve the source documents the AI should have used. Compare the output against these sources using an LLM or semantic similarity. Flag significant deviations.
Validate high-stakes claims only
Classify claims by type and only validate the high-risk ones. Pricing always gets validated. General explanations might not. This balances thoroughness with speed.
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What type of AI output are you validating?
A customer asks about returns. The AI generates a response claiming "30-day returns on all items." But the actual policy has exceptions. Factual validation checks the claim against the policy document and corrects the response before delivery.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Your knowledge base has the old pricing from last quarter. The AI correctly states new pricing, but validation "corrects" it back to the outdated price. You are now confidently wrong, with validation as the cause.
Instead: Treat source freshness as a first-class concern. Tag documents with last-verified dates. Escalate when sources are stale rather than blindly trusting them.
The AI says "returns must be within two weeks." Your policy says "14-day return window." Validation finds no exact match and flags it as unverified, even though it is correct.
Instead: Use semantic matching, not just keyword search. Validate meaning, not phrasing. Allow for paraphrasing when the facts are equivalent.
Validation catches that a product description says "approximately 10 inches" when the spec sheet says "9.8 inches." It blocks the response while customers wait. The error did not matter.
Instead: Classify claim types by business impact. Wrong pricing gets blocked. Approximate measurements get flagged. Stylistic differences get ignored.
Factual validation checks AI-generated outputs against source documents and known facts before they reach users. It extracts claims from the AI response, searches for supporting evidence in your knowledge base, and flags statements that cannot be verified or contradict sources. This prevents the AI from presenting fabricated information as fact.
Factual validation works in three steps: First, it extracts verifiable claims from the AI output (names, numbers, dates, policies). Second, it searches your knowledge base for evidence supporting or contradicting each claim. Third, it either flags unsupported claims for review, corrects them from source, or blocks responses that fail verification thresholds.
Use factual validation when incorrect AI outputs carry real consequences. This includes customer-facing support bots (wrong policies, pricing), internal knowledge systems (outdated procedures), compliance-sensitive contexts (legal, financial), and anywhere the AI references specific facts from your documentation. Skip it for creative tasks where accuracy to sources is not the goal.
Hallucination detection identifies when AI generates content not grounded in any input. Factual validation goes further by checking outputs against authoritative sources. An AI might not hallucinate (the information exists somewhere) but still be factually wrong for your context. Factual validation catches both issues by verifying against your specific knowledge base.
The most common mistake is validating against outdated sources. If your knowledge base has stale documentation, validation confirms wrong facts as correct. Another mistake is only checking exact matches, missing paraphrased claims. A third is treating all facts equally when some errors (like pricing) matter more than others.
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Choose the path that matches your current situation
You have no validation on AI outputs today
You are validating some outputs but coverage is inconsistent
Validation is working but you want better coverage or speed
You have learned how to verify AI outputs against source documents. The natural next step is understanding how to track which sources informed each response, enabling transparency and debugging.