You stop AI from inventing facts by never letting it supply facts in the first place — you give it the facts yourself, then ask it only to organize and phrase them.
Hallucination is not a bug you can prompt away with "be accurate." It happens because a language model predicts the most plausible next words, and plausible is not the same as true. So the fix is structural: change what the AI is allowed to decide. If the model never has to reach for a fact, it never has to guess one.
Here is the mechanism behind that. A model like ChatGPT, which our AI tool database lists as OpenAI's flagship assistant running GPT-5.5 with a 1M-token context window, does not look up facts the way a search engine does. It generates text token by token based on patterns.
When you ask "what percentage of small businesses use AI?" with no source attached, the model produces a number shaped like a real statistic. It sounds authoritative because it learned what authoritative statistics sound like.
The larger the context window, the more raw material you can hand it — a 1M-token window can hold entire reports — which is exactly why feeding it your source text works so well. You are not making the model smarter. You are removing its need to improvise.
A concrete worked example. Say you are writing a blog post about AI pricing and you want to mention ChatGPT's plans. Instead of asking the model to "list ChatGPT's pricing," you paste in the verified snapshot: Free tier uses GPT-4o mini, Plus is $20 per month, Pro is $200 per month.
Then you prompt: "Using only the pricing details above, write two sentences comparing the tiers for a beginner audience. Do not add any number I did not give you." The model now has nothing to invent.
Compare that with the lazy version — "write about ChatGPT pricing" — where you will often get a confident, wrong figure that costs you reader trust the moment someone checks. The same approach works for Claude, which our database lists at Free, $17 per month billed annually or $20 monthly on Pro, and Max from $100 per month. Paste the numbers, then let the model write.
Two habits make this stick. First, the "closed-book" rule: tell the model explicitly that anything not in your pasted text should be answered with "not provided" rather than a guess. Second, a verification pass where you highlight every number, name, date, and quote in the draft and confirm each one traces back to your source.
This is the step most people skip, and it is the only step that actually catches a fabrication. A useful tip beyond the obvious: ask the model to quote the exact sentence from your source that supports each claim. If it cannot produce the sentence, the claim is probably invented — models are far better at copying text you gave them than at fabricating a matching citation.
Where this fails. It costs time. Pasting sources and checking claims turns a two-minute draft into a fifteen-minute one, and for low-stakes writing like a personal email that trade is not worth it.
It also breaks down when your source material is itself wrong — the model will faithfully repeat your bad data, and it will not warn you. And no method makes a model reliable for facts it was never given, especially recent events, niche statistics, or anything after its training cutoff.
If you need a fact you do not have, find it from a primary source yourself; do not ask the model to fill the gap. Treat the AI as a writer and editor, never as a reference book, and the fake-fact problem mostly disappears.