[Steve Side Hustler]

I often get articles, summaries or images from AI tools and want to make sure they’re trustworthy. I’m not technical, so I’m looking for simple, practical ways to check for factual errors and bias without diving into complex methods.

What quick steps or tools do you recommend for a non-technical person to:

• Check basic facts (dates, names, claims)
• Spot potential bias or one-sided language
• Verify sources the AI might have used

Examples, friendly tricks (like questions to ask the AI), or reliable, easy-to-use websites/apps would be very helpful. If you have a short checklist you follow, please share it.

Thanks — I’m hoping to build a simple routine so I can trust what I share or use.

[Jeff Bullas]

Hook: AI can write fast — but speed doesn’t mean accuracy. Here’s a practical checklist to verify AI-generated content for facts and bias so you can publish with confidence.

Quick context: You don’t need to be a tech expert. Verification is a mix of simple checks, targeted questions, and a few smart tools. Think of it as editing with a fact-safety lens.

What you’ll need:

• Original AI text you want to verify.
• Two credible sources (news site, academic paper, government report).
• Access to a search engine and a second AI or fact-check tool.
• Time: 10–30 minutes per short article.

Step-by-step verification:

1. Read the AI text and underline factual claims (names, dates, statistics, cause-effect statements).
2. For each claim, search. Use the original source where possible (study, report, press release). Note discrepancies.
3. Ask the AI to list its sources and confidence level for each claim. If it can’t, flag the claim for manual verification.
4. Check for bias: who benefits from the claim? Is a single perspective presented as fact? Ask for opposing viewpoints.
5. Fix the text: correct facts, add citations, and add hedge language where uncertainty exists (e.g., “studies suggest” rather than “proves”).
6. Final sanity check: have a colleague or a different AI review your corrected version for clarity and remaining bias.

Copy-paste AI prompt (use with your AI assistant):

“You are a fact-checker. For the following text, list each factual claim, provide a short verification (source and URL if available), give a confidence rating (low/medium/high), identify potential bias or missing perspectives, and suggest one sentence to correct or qualify the claim.”

Worked example:

AI text: “The new health app reduced hospital visits by 40% in 2024.”

1. Claim: 40% reduction. Search for study or press release. If no primary source, mark as unverified.
2. Bias check: Was the study run by app maker? Is the sample size small?
3. Fix: “A small study funded by the app developer reported a 40% reduction; independent verification is pending.”

Mistakes people make & how to fix them:

• Do not accept AI citations at face value — double-check sources.
• Do not remove uncertainty; instead, label it. Add context when evidence is limited.
• Do use multiple sources and perspectives to reduce bias.

Quick action plan (next 24–48 hours):

1. Pick one AI-generated article you plan to publish.
2. Run the copy-paste prompt above and verify the top 5 claims.
3. Edit to add citations and qualifiers, then get a second review.

Remember: Verification is partly habit. A short routine — claim, check, correct — protects your credibility and saves time long term.

[aaron]

Quick win (under 5 minutes): Paste your AI text into the prompt below and have the assistant return the top 5 factual claims with confidence ratings — you’ll get a short verification checklist to act on immediately.

Good point in your note: asking the AI for its sources and confidence is an essential first filter. I’ll build on that with a results-focused workflow so you can turn verification into a repeatable KPI.

Why this matters: Publishing unchecked AI content damages credibility and conversion. A 5–10% factual error rate can halve trust and increase corrections, costing time and reputation.

What you’ll need:

• AI-generated text (the piece you’ll publish).
• A browser / search engine and access to one other AI or fact-check tool.
• A simple doc or spreadsheet to track claims and sources.
• 10–30 minutes per short article.

Step-by-step (do this every time):

1. Read the text and underline all factual claims (names, dates, stats, causal statements).
2. Run this copy-paste prompt against the AI to extract claims and confidence.
3. For each claim: find a primary source (study, press release, official stat). Note mismatches.
4. Flag claims with no primary source or low confidence for removal or qualification.
5. Edit the article: add citations, hedge language where needed, and include a short note on methodology for readers if relevant.
6. Final check: have one colleague or a second AI scan the edited version for remaining errors or bias.

Copy-paste AI prompt (use as-is):

“You are a fact-checker. For the following text, list each factual claim (briefly), provide a confidence rating (high/medium/low), name the best primary source to verify it (study, report, or official data) and summarize the source in one sentence, identify any obvious bias or missing perspective, and suggest one precise sentence to fix or qualify the claim for publication.”

Metrics to track (targets):

• Accuracy rate: % of top-10 claims verified by primary sources — target >90%.
• Time per article: target <30 minutes for short pieces.
• Bias flag rate: % of articles with at least one flagged perspective — track trend down or up.
• Corrections after publish: target = 0 major factual corrections per 100 articles.

Common mistakes & fixes:

• Accepting AI citations verbatim — fix: verify the primary source yourself.
• Removing uncertainty to make copy punchier — fix: use hedges or cite the study limitations.
• Relying on one source — fix: add an independent corroborating source where possible.

One-week action plan:

1. Day 1: Pick one AI article you plan to publish; extract top 10 claims with the prompt.
2. Day 2: Verify the top 5 claims; add citations in the doc.
3. Day 3: Edit copy to include qualifiers and source notes.
4. Day 4: Peer review or second-AI scan; resolve remaining flags.
5. Day 5: Publish and log metrics; Day 6–7: review outcomes and adjust thresholds.

Expect immediate wins: fewer post-publication corrections, faster editorial reviews, and higher reader trust. Track the three metrics above weekly and adjust the checklist when error patterns appear.

— Aaron

Your move.

[Jeff Bullas]

Quick win (under 5 minutes): Paste your AI text into the prompt near the end and ask for the top 5 factual claims with confidence ratings. You’ll get a short checklist to act on immediately.

Good call from Aaron — asking the AI for sources and confidence is an essential first filter. Here’s a compact, practical workflow you can use every time, with a bias check and a simple credibility score so verification becomes routine, fast and measurable.

What you’ll need:

• The AI-generated text you want to verify.
• A browser / search engine and one other AI or fact-check tool.
• A simple doc or spreadsheet to track claims, sources, and a credibility score.
• 10–30 minutes for a short article.

Step-by-step (do this every time):

1. Read the text and underline factual claims: names, dates, statistics, cause-effect statements.
2. Run the extraction prompt (below) to list the top 5–10 claims with confidence ratings.
3. For each claim, find a primary source (study, official stat, press release). Note: prefer primary over secondary reporting.
4. Apply a quick credibility score per claim: 2 points = primary source + independent confirmation, 1 = single credible source, 0 = no reliable source.
5. Run a 3-question bias test: (a) Who benefits? (b) Is one viewpoint missing? (c) Is language hedged appropriately?
6. Edit the copy: correct facts, add citations, and add qualifiers (e.g., “studies suggest” or “one study reported”).
7. Final check: ask a colleague or a second AI to scan the edited version for remaining errors or bias.

Example (worked):

AI text: “Remote work lifted team productivity by 25% in 2023.”

1. Claim: 25% productivity increase. Search for original study or company report. If found, check sample size and who funded it.
2. Credibility score: primary study + independent replications = 2; only vendor report = 0–1.
3. Fix: “A 2023 study by X found a 25% boost in productivity in their sample; independent confirmation is limited, and results may not generalize.”

Common mistakes & fixes:

• Accepting AI citations verbatim — fix: open the cited source and confirm the claim matches the original.
• Removing uncertainty to sound decisive — fix: keep hedges when evidence is limited.
• Using a single source — fix: add at least one independent corroborating source when possible.

Copy-paste AI prompt (use as-is):

“You are a fact-checker. For the following text, list the top 8 factual claims (brief), give a confidence rating for each (high/medium/low), name the most relevant primary source to verify it or write ‘none found’, give one-sentence evidence summary, flag any obvious bias or missing perspective, and suggest one precise sentence to correct or qualify the claim for publication.”

Action plan — next 24–48 hours:

1. Pick one AI-generated article you plan to publish.
2. Run the prompt above and verify the top 5 claims in your doc.
3. Edit to add citations and qualifiers; get one colleague or second-AI review.

Remember: A short routine — extract, check, score, correct — protects your credibility. Start with one article today and you’ll build a verification habit that saves time and builds reader trust.

[Rick Retirement Planner]

Nice point: I like the emphasis on extracting the top claims and giving each a quick credibility score — that single step turns a vague edit into an actionable checklist. Clarity like that builds confidence for any non-technical editor.

What you’ll need:

• Your AI-generated text.
• A browser / search engine and a second checking tool (another AI or a fact-check site).
• A simple doc or spreadsheet to log claims, sources, and a confidence score.
• Time: plan 5–10 minutes per important claim (10–30 minutes for a short article).

One concept in plain English: a “primary source” is the original place a claim comes from — the study, official report, company press release or dataset — not a news story or blog that repeats it. Always try to trace back to that original document.

How to do it — step-by-step:

1. Read once and mark every factual claim: names, dates, statistics, causal statements.
2. For each claim, search for the primary source. Practical searches: put exact phrases in quotes, add the author or year, or use site:gov / site:edu to find official data. If you find only secondary reporting, flag it.
3. Quickly check the source: who funded it, sample size, publication date, and whether independent teams replicated it. Note any conflicts of interest.
4. Give each claim a simple score (High / Medium / Low) or 2/1/0: 2 = primary source + independent confirmation; 1 = single credible source; 0 = no reliable source found.
5. Run a short bias test: who benefits from this claim, is an opposing view missing, and is the language overstated? Add a short note if a perspective is absent.
6. Edit the copy: correct or remove unsupported claims, add a citation or a one-line qualifier (e.g., “one small study found…”), and keep hedging when evidence is limited.
7. Final check: have a colleague or a second AI scan the edited piece for anything you missed.

What to expect:

1. Some claims will be easy to verify; others may be untraceable—that’s normal. Treat unverified claims as removable or qualifying language opportunities.
2. Don’t be surprised if vendor-funded studies dominate some topics; call that out and look for independent confirmation before publishing strong claims.
3. With this routine you should cut post-publication corrections and build reader trust; measure time per article and % of top-10 claims verified to track progress.

Tip: Start by verifying the 3–5 biggest claims that would most damage credibility if wrong — that gives the best protection for the time invested.

[Author]

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