MecraFact-checking AI scripts: how to catch wrong information before it reaches the video
AI writes fluently but can invent facts. A safe flow: ground scripts in sources, check them separately, list every claim, and never ship a script that fails.
When you tell a language model "write a video script about X", the output is usually fluent, convincing and confident enough to be wrong. A wrong date or an invented number looks small in one video; but with automation it multiplies into dozens of videos and ends the channel's credibility. That is why fact-checking in AI production is not a luxury; it is the workflow itself.
What is hallucination and where does it show up most?
Hallucination is when a model produces information that doesn't exist as if it were real. It is seen most in these areas:
- Dates and numbers. For example confusing the Eiffel Tower's construction starting in 1887 and finishing in 1889 with "opened in 1887".
- Absolute statements such as "first", "only", "largest". For example saying Marie Curie won two Nobels in the same field; in fact one is in Physics (1903) and the other in Chemistry (1911).
- Names and titles. Mixing up names of people and institutions, or their posts.
- Quotes and sources. Citing a source that doesn't exist or attributing a quote nobody said.
- Recent developments. Presenting outdated information the model doesn't know about as current.
7 principles of a safe flow
1. Tie the script to a source
Instead of telling the model to "write what you know", give it a reliable source text (for example the relevant Wikipedia section) and ask it to rely only on the information in it. If something is not in the source, it is out of bounds.
2. Separate the writing and the checking stage
If the stage that writes the script checks its own output, it repeats its own errors. The check should be a separate pass: the source text and the draft are given together and the checker is asked "is every claim in the draft supported by the source?".
3. Extract the claims one by one
Don't check with a general question like "is the script right?"; list the verifiable claims (dates, numbers, names, cause and effect) in the draft one by one and match each with the source. A claim that doesn't match is corrected or removed.
4. Add numeric and logical checks
Some errors are easily caught by rule-based checks: are year ranges consistent, are units right, do percentages add up, is the sentence that says "first" really absolute? Checking these with deterministic code or simple scripts can be more reliable than an AI check.
5. Calibrate certainty
If the source says "approximately" or "is estimated", the script should say so too. Turning uncertain information into a definite statement is the most insidious form of hallucination.
6. A script that fails does not become a video
Don't let a script that doesn't pass the check turn into a video; "I'll look later" in automation means the error goes live. Make it a rule: a script that fails the check is not taken into production.
7. Human sampling
However good the automation you build, watch a video yourself now and then: all of the first videos, then one in a while. The human eye catches tone, context and oddities the machine doesn't notice.
Limits: checking doesn't reduce the risk to zero
No method reduces mistakes to zero; it lowers the risk. The source itself can also be wrong. Be extra careful in these areas:
- Health, law, finance: YouTube doesn't consider AI personas giving expert-style advice here eligible for monetization (policy summary). Give general information, cite sources and avoid personalized advice.
- Fast-changing topics: election results, prices, current statistics. Look at the date of the source.
- Disputed topics: state that there is more than one point of view.
Citing sources builds trust
Showing real photos and information with their sources tells viewers and YouTube "this channel takes its work seriously". Write the sources in the video description; if a photo requires attribution, add it (licensing guide).
If a mistake is found
If you notice an error after publishing: (1) fix the video or description, (2) if needed announce the correction with a pinned comment, (3) fix the step that produced the same error (source, prompt, check rule). Seeing that your channel admits its mistake raises viewer trust.
Fact-checking in Mecra
Mecra grounds the script in information taken from Wikipedia and runs it through a fact-check separate from the writing; a script that fails the check doesn't become a video. When a scene names a person, team or place, a real photo is shown with its source. Still, no tool can zero out wrong information: watching the first videos and taking extra care on sensitive topics is recommended. For the general risks of automation see what is YouTube automation?.
Sources
Frequently asked questions
Why does AI produce wrong information?
Language models produce plausible text probabilistically; they don't separately test whether a fact is true. So they can make mistakes especially in dates, numbers, names and claims like “first” or “largest”; this is called hallucination.
Is checking with a second AI enough?
Not by itself, but it helps. The checking stage should be separate from the writer and should test claims against a supplied source text, not the model's memory. On critical topics a human check is also needed.
Is Wikipedia a reliable source?
It is a good starting point for general facts; but anyone can edit it and it may contain errors on fast-changing or disputed topics. For critical claims look at primary sources (official bodies, academic publications).
What should I do if I find a mistake after publishing?
Update the video or add the correction to the title, description and a pinned comment. If needed, hide the video and re-upload it. Leaving the error silently damages trust.
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