How to Tell If Content Was Written by AI (A Simple, Reliable Guide for Everyday Internet Users)

You usually can’t prove something was written by AI just by reading it—you can only estimate likelihood and then verify the claims. AI detectors also aren’t definitive: they can misclassify text (false positives and false negatives), so treat any score as a clue, not a verdict.​

A useful mindset: “Is this content trustworthy?” is often a better question than “Was this made by AI?”

The 2-minute AI-content triage (works on posts, emails, reviews)

Follow this in order—each step catches a different failure mode.

1. Scan for substance in 20 seconds
   Look for specific, checkable details: names, dates, numbers, locations, screenshots, first-hand constraints, or citations. AI-written fluff often sounds complete while staying vague.
2. Check for “experience fingerprints.”
   Humans usually include small frictions: what went wrong, what surprised them, what they tried first, what they’d do differently. AI text often skips the messy middle and jumps to a neat conclusion.
3. Do a single claim-verification search
   Pick one concrete claim and search it (or search a unique sentence in quotes). If you can’t corroborate anything meaningful, lower trust.
4. Look at the source, not just the words
   Is there an author? An about page? A real profile history? A site with contact details and editorial standards? Provenance beats vibes.
5. Only then use a detector (optional)
   Use tools as a second opinion—especially when you’re deciding whether to spend money, share the post, or follow advice. Remember: detectors are probabilistic and inexact.​

Common “tells” in AI-written content (and what they really mean)

These signals aren’t proof. They’re patterns that should push you toward verification.

1) Over-structured, over-smooth writing
AI text often reads like it was optimized for clarity: tidy headings, balanced bullet points, and evenly paced sentences. That’s not inherently bad—some humans write that way—but if it’s paired with low specificity, be cautious.

2) Generic certainty without evidence
Watch for strong claims with no sources, no data, and no “how we know.” A trustworthy human post usually signals uncertainty where appropriate (“in my case,” “depends on,” “I couldn’t verify X”).

3) Repetition without adding new information
A classic pattern is to rephrase the same point 3–4 times to appear comprehensive. If each paragraph could be deleted without losing meaning, you’re likely reading generated filler.

4) “Perfectly neutral” tone in emotionally loaded contexts
Customer complaints, medical scares, legal advice, and crisis updates written in flat, generic language can be a red flag. People typically exhibit context-specific emotion or urgency.

5) Hallucination-shaped errors (confident but wrong details)
AI systems can produce plausible-sounding details that don’t exist. This is why verifying one claim is so powerful: it quickly distinguishes “well-written” from “reliable.”

Stronger signals than writing style: information quality checks

If you want one upgrade that beats most “AI detectors,” it’s this: evaluate the information.

• Source traceability: Are there links to primary sources, and do they actually support the claim (not just “related reading”)?
• Date relevance: Does the post clearly match the current year/version/product model?
• Constraint handling: Does the advice mention prerequisites, edge cases, and failure modes—or does it pretend everything works smoothly?
• Original artifacts: screenshots, logs, benchmark tables, photos, and code snippets with context (not just copy-and-paste blocks).

Using ZeroGPT (and other detectors) responsibly

If you decide to use an AI detector, use it like you’d use a spell-checker: helpful, but not authoritative.

ZeroGPT offers features such as sentence highlighting, an “AI percentage” gauge, multi-language support, file uploads, and reports—useful for reviewing where a detector identifies AI-like patterns, not for “proving” authorship.

Its site also describes a multi-stage detection approach intended to optimize accuracy while minimizing false positives/negatives, but it’s still making an estimate—not establishing ground truth.​

A practical way to use a detector score

• Low score: Don’t assume it’s human; still verify important claims.
• Medium score: Treat as “uncertain”; rely more on provenance and claim-checking.
• High score: Assume it might be AI; verify before sharing/acting, and avoid using it as the sole basis for accusations.

Why you shouldn’t rely on one tool
Even major AI detection efforts have struggled with accuracy—OpenAI discontinued its own AI text classifier in 2023, citing low accuracy. More broadly, AI detection is not foolproof; false positives and negatives are possible, and results should be treated as probabilities rather than proof.

What to do when content seems AI-written

Your response should match the risk.

• Low stakes (entertainment, casual opinions): Ignore the authorship question and focus on whether it’s enjoyable or useful.
• Medium stakes (product recommendations, “how-to” guides): Cross-check key claims, prefer sources with transparent authorship, and look for primary documentation.
• High-stakes (health, finance, legal, safety, security): Don’t act on it without independent verification; prefer official sources and credentialed experts; consider consulting a professional.

When not to accuse someone of using AI
If you don’t control the platform (e.g., social media) or the decision has consequences (employment, education, reputation), don’t treat detector scores as evidence. AI detectors can misclassify human writing, and overreliance can cause unfair harm.​

A simple decision framework (bookmark this)

Ask three questions:

1. Does it contain checkable specifics? (If no → suspicious)
2. Can I quickly verify one key claim? (If no → lower trust)
3. Is the source credible and transparent? (If no → treat as untrusted)

If 2 out of 3 fail, assume it’s low-trust content—whether it’s AI-written or just low-effort human writing.