Verifying What AI Says About Pricing and Plans
A price from an AI is a snapshot of a snapshot. Check it against the vendor’s own page, watch for tier conflation, and describe structure instead of numbers.
A price from an AI is a snapshot of a snapshot. Check it against the vendor’s own page, watch for tier conflation, and describe structure instead of numbers.
A copyable fact-checking checklist built for one person who is both writer and checker, ordered by cost, ending with a written record of what you did not check.
There is no average, and anyone quoting one is guessing. What determines the time is how many checkable claims the draft contains, not how many words.
You cannot check everything in an AI draft. Rank claims by consequence if wrong and likelihood the model got it wrong, then cut what you cannot afford to check.
Agreement between two models is weak evidence. Disagreement is strong evidence. The two are not symmetric, and that asymmetry is the only useful thing here.
A three-step batch method for testing the links in an AI draft, ordered cheapest check first, so twenty citations take minutes instead of an afternoon.
A URL is the easiest thing for a language model to invent convincingly, and the hardest thing for a reader to doubt. Here is why, and what the four failure types look like.
Adding “cite your sources” to a prompt changes what the output looks like, not where it came from. Here is what asking actually buys you, and what it does not.
How to tell a primary source from a secondary one in an AI draft, and why tracing a citation back to its origin matters more than usual here.
Why AI tools get calculations wrong and a concrete method for checking any math, percentage, or total in an AI-written draft before you publish it.