Large language models generate text by predicting plausible continuations, not by looking up verified facts in a database, which means a confident, well-written sentence and an accurate one are produced by exactly the same process, and nothing about the model's tone reliably distinguishes them. That's the single most important thing to internalize before publishing anything AI-assisted, and it's the basis for every technique below.

1. Separate claims into categories by verification difficulty

Not every sentence in a piece of AI-generated text carries the same risk. General, well-established background information is lower-risk. Specific numbers, dates, quotes, and named entities are higher-risk. These are exactly the kind of details a model can generate fluently and plausibly while getting subtly wrong. Triage your fact-checking effort toward the second category; that's where the actual risk concentrates.

2. Verify every specific citation independently

Never treat an AI-cited source, quote, or statistic as verified just because it's presented with a specific-sounding attribution. This is the single most well-documented failure mode across current AI systems, and it's not a hypothetical concern: fabricated legal citations generated by AI tools became a widely publicized problem in real court filings, which is a direct part of why law firms adopting AI research tools like the ones covered in our piece on Harvey and CoCounsel remain insistent on independently verifying every citation before anything is filed. Before publishing, independently locate and confirm every specific citation exists and says what the AI claimed it says.

3. Distrust confident specificity most, not least

Counterintuitively, a highly specific, confidently stated claim deserves more scrutiny than a vague one, not less. Specificity is exactly what makes an error most damaging and exactly what a model is most likely to fabricate plausibly when it doesn't actually have reliable grounding for a claim. "Approximately half" is lower-risk than "53.7%," even though the second sounds more rigorous; verify the second more carefully, not less.

4. Check internal consistency across a long piece

AI-generated long-form content occasionally introduces internal contradictions, a number stated one way early in a piece and differently later. This is a distinct failure mode from factual inaccuracy and is worth a dedicated read-through specifically looking for consistency, separate from checking individual facts against outside sources.

5. Apply extra scrutiny to anything you already believed

The hardest errors to catch are the ones that confirm what you already expected to be true. You're less likely to pause and verify a claim that matches your prior assumptions, which is exactly the condition under which a plausible-but-wrong AI-generated claim is most likely to slip through review unchecked. Deliberately flag claims that align suspiciously well with what you already believed for extra verification, not less.

Why this discipline matters beyond any one article

This isn't a one-time checklist to apply to a single risky piece. It's a standing editorial discipline that should apply to any AI-assisted content before publication, the same way a professional editor fact-checks a human writer's draft rather than trusting it on the strength of good prose. If you're building AI assistance into a broader workflow (for coding, for research, for any task where output quality matters) the same underlying principle applies; see our guide to setting up an AI coding assistant for how this discipline extends to code review specifically, and for the editorial policy Inference itself follows on exactly this question, see our Editorial Standards and AI Disclosure pages.