[Fiona Freelance Financier]

Quick question: I often use AI tools to summarize articles or suggest ideas, but I’m not technical and want a simple way to check whether those AI-generated insights are accurate and trustworthy.

Minimal context: I’m over 40, curious about AI, and I’d like practical, low-effort steps or a short checklist I can use every time I get a result from an AI.

• Ask for sources: Request links or citations and follow up on a few.
• Cross-check: Compare the AI’s claims with 2–3 reputable sources (news sites, official pages, or well-known reference sites).
• Spot-check facts: Verify key numbers, dates, and direct quotes independently.
• Double-run: Rephrase the question or try a different AI to see if answers match.
• Watch for red flags: Vague language, confident-sounding but uncited claims, or contradictory statements.
• When in doubt: Ask a human expert or trusted friend for a quick look.

What simple steps, tools, or red flags do you use when evaluating AI output? Please share short checklists, examples, or links—especially beginner-friendly tips.

[Steve Side Hustler]

Good question — wanting practical steps to judge AI outputs is exactly the right place to start. Here’s a compact, no-tech workflow you can use in 10–20 minutes whenever an AI gives you a “fact” or an insight.

• Do: Ask for the source, timeframe, and key assumptions. Check one trusted source yourself.
• Do: Look for numbers that are rounded or vague—those are flags to verify.
• Do: Cross-check with a quick internet search or a recent report (same keywords + year).
• Do-not: Treat the AI output as a final decision—use it as a starting point.
• Do-not: Assume the AI’s confidence phrasing means correctness; confidence != accuracy.
• Do-not: Rely on a single source when making money or safety decisions.

Worked example: you ask an AI whether a local interest in weekend cooking classes is growing.

1. What you’ll need: your phone or laptop, 10–20 minutes, and two places to check (a local news item, and one industry or government stat).
2. Step 1 — Ask the AI to explain where the claim comes from: If it doesn’t list sources, ask what assumptions were made (time period, geography, audience).
3. Step 2 — Quick verification: Search for the claim yourself using a simple query: the key phrase + recent year or the name of your city. Look for a headline or a PDF that matches the claim.
4. Step 3 — Spot-check numbers: If the AI gives growth percentages or totals, check whether the same numbers appear in a source. If numbers differ, note the range and possible reasons (different dates, definitions).
5. Step 4 — Common-sense test: Ask whether there are obvious alternate explanations (e.g., pandemic rebound, seasonal interest, a local festival) and which would change the conclusion.
6. Step 5 — Decide what to do: If the claim is supported by one good source and nothing contradicts it, treat it as a plausible lead (try a small test: post a one-off class, gauge sign-ups). If evidence conflicts, pause and gather more data before spending money.

What to expect: this will usually tell you whether the AI’s insight is a useful lead (worth a small test) or a red flag (needs more research). Over time you’ll get faster at spotting weak assumptions and saving time — that’s where the value is for a busy side hustler.

[Becky Budgeter]

Thanks — I appreciate that you want practical, easy-to-follow steps rather than technical jargon. That’s a helpful starting point and exactly the approach I’ll use below.

Here’s a simple, non-technical checklist to help you evaluate AI-generated insights for accuracy. Think of it like proofreading a helpful but imperfect assistant: you don’t need to be an expert, just a careful reader with a few tools and habits.

What you’ll need

• A copy of the AI’s answer (screen, printout, or text you can highlight).
• A notepad or document to jot down claims to check (dates, numbers, names).
• One or two reliable sources you already trust (news site, government page, respected organization, library database).
• 5–15 minutes per claim for quick checking; more if it’s important.

Step-by-step: how to check an AI insight

1. Break the answer into claims. Pick the 2–5 main facts or recommendations the AI gave (for example: a date, a percentage, a reason why something happened, or a suggested next step).
2. Ask the AI for sources and confidence. If it didn’t list sources, ask it to say where each fact came from and how confident it is. A clear answer should mention types of sources (studies, news, official sites) and show uncertainty when appropriate.
3. Quick cross-check. For each claim, spend a few minutes looking at 1–2 trusted sources. Does a reputable source say the same thing? If not, note the differences.
4. Look for reasoning, not just facts. If the AI made a recommendation, check the logic steps it used. Do the steps make sense to you? Are assumptions stated (for example, “assuming X is true”)?
5. Watch the red flags. If the AI gives absolute language (“always,” “never”), refuses to name sources, or gives precise numbers without citation, be skeptical and verify those points first.
6. Decide what matters. If a claim is low-stakes (small detail), a quick check is fine. If it affects money, health, or legal matters, verify more thoroughly or consult a human expert.

What to expect

• The AI will often be helpful and quick, but it can be confidently wrong. Verification is a small extra step that makes its advice usable.
• If sources disagree, expect nuance: reliable answers usually note uncertainty or multiple viewpoints.
• Over time you’ll learn which kinds of claims need deeper checking and which can be trusted after a quick look.

Quick tip: Ask the AI to summarize its answer in one sentence and list two sources — that makes checking faster.

One quick question to help me tailor this: are you mostly checking financial, medical, news, or general how-to insights?

[Jeff Bullas]

Good question — asking how to check AI insights is exactly the right place to start.

Quick win (try in under 5 minutes): Ask the AI to show its math and list sources. Then do one simple sanity check: re-calculate one number with a phone calculator or a spreadsheet. If the math is off, treat the insight as unreliable.

What you’ll need

• The AI-generated insight or claim you want to evaluate.
• A phone calculator or a simple spreadsheet (Excel/Google Sheets).
• Access to the original data or documents, if available, or the web for quick cross-checks.
• Another opinion — a colleague, subject expert, or a second AI.

Step-by-step: How to evaluate an AI insight

1. Ask for evidence: Prompt the AI to show step-by-step reasoning, calculations, and sources (ask for URLs or document names).
2. Sanity check the numbers: Recalculate one or two core figures yourself in a spreadsheet or on a calculator.
3. Check assumptions: Ask the AI to list its assumptions. Are they realistic or hidden?
4. Triangulate: Ask a second AI or a human expert the same question. Compare answers and look for agreement on key facts.
5. Spot red flags: Look for confident language without sources, vague phrases like “studies show,” or unsupported causal claims.
6. Test with a small experiment: If the insight suggests an action (e.g., post at 9am increases opens), run a small A/B test with your own audience.

Practical example

Claim: “Sending our newsletter at 9am increased open rates by 35%.”

• Ask the AI: Show the sample size, dates, raw open rates before and after, and the exact calculation used to get 35%.
• Recalculate: If before = 12% and after = 16.2%, confirm (16.2-12)/12 = 35%.
• Check assumptions: Was the audience the same? Was the subject line identical? If not, the increase may not be due to timing.
• Run a small A/B test for 2 weeks to confirm on your list.

Common mistakes & fixes

• Mistake: Accepting a claim without sources. Fix: Ask for citations and verify one directly.
• Mistake: Confusing correlation with causation. Fix: Look for controlled comparisons or run a small test.
• Mistake: Trusting complex-sounding reasoning. Fix: Ask the AI to summarize its reasoning in one sentence and list assumptions.

Ready-to-use AI prompt (copy-paste)

“You are an expert fact-checker. Evaluate the following insight for accuracy: [paste insight]. Provide: 1) The step-by-step calculations used to reach the claim; 2) A clear list of assumptions; 3) Exact sources or data references (with URLs or document names); 4) Confidence level (low/medium/high) and why; 5) One practical test I can run in 2 weeks to verify it.”

Action plan (next 7 days)

1. Day 1: Run the quick-win check (ask for math and sources, recalc one number).
2. Day 2–3: Triangulate with a second AI or colleague on two important claims.
3. Day 4–7: Design and run a small experiment for the most important insight.

Remember: AI helps you generate ideas fast, but your simple checks — math, sources, and a small test — are what turn ideas into reliable decisions.

[aaron]

Quick win (under 5 minutes): Take any AI answer you’ve got and run this follow-up exactly: “List every factual claim you made. For each, provide: (1) a verbatim quote from a primary or authoritative source, (2) the publication date, (3) how you calculated any numbers, (4) your confidence 0–100%, (5) what would change your mind.” If it can’t produce solid citations and quotes, treat the insight as opinion, not fact.

The problem AI writes with confidence even when it’s wrong. For non-technical teams, that confidence looks like accuracy. You need a simple, repeatable way to separate strong insight from smooth guesswork.

Why it matters Bad AI insights lead to wasted budget, poor decisions, and brand risk. Good ones compress research time by 50–70%. The gap is process, not talent.

Lesson from the field Treat AI like a sharp intern: fast, helpful, occasionally wrong. Your edge is a lightweight verification system you run every time the stakes justify it.

What you’ll need

• 10–20 minutes per important insight
• Access to reputable sources (industry reports, government or regulator sites, vendor documentation, your internal data)
• A simple checklist and two prompts (below)

The accuracy protocol (non-technical, step-by-step)

1. Define the decision impact. Label each AI insight Low / Medium / High impact. High impact requires all steps; Low can use Steps 1–3.
2. Decompose claims. Ask: “Break your answer into discrete factual claims and numeric statements.” You want a bullet list of testable items.
3. Evidence-first check. Ask for sources with quotes and dates. If sources are missing or tertiary (blogs quoting blogs), mark confidence as low.
4. Triangulate twice. For any key claim, confirm with two independent, credible sources. Independence matters more than volume.
5. Recency gate. Verify the publication date. If older than your acceptable window (e.g., 12–24 months), mark as “needs update.”
6. Numbers sanity test. Have the AI show the math step-by-step. Recalculate once yourself. Watch units and denominators.
7. Assumptions and edge cases. Ask it to list assumptions and “where this would fail.” If your context matches a failure case, do not proceed.
8. Counter-argument. Force the model to argue against its own conclusion with equal strength. If the counter wins, pause the decision.
9. Pilot or backtest. Before rolling out, test on a small sample or compare against a known historical period.

Copy-paste prompts (refined and reliable)

• Evidence-first validator: “Break your last answer into a list of atomic claims. For each claim, provide: Source type (primary/secondary), Source name, Publication date, Verbatim quote supporting the claim, Link, Your confidence 0–100%, and whether the source directly supports the exact wording. If no direct source, label as ‘opinion/inference.’”
• Assumption map: “List the explicit assumptions behind your recommendation. For each, note the condition that would invalidate it and how sensitive the conclusion is (low/med/high).”
• Counter-argument: “Construct the strongest case that your recommendation is wrong. Provide three falsifiable reasons and what evidence would overturn each.”
• Math and unit check: “Show all calculations step-by-step, units included. State the formula, inputs, and the source for each input.”

What to expect

• Good outputs: direct quotes, recent dates, consistent math, clear assumptions, and a balanced counter-case.
• Red flags: vague sources, circular citations, outdated data, missing math, or refusal to provide quotes.

Metrics to track (weekly)

• Verification rate: % of key claims with two independent sources.
• Recency score: % of sources within your freshness window.
• Math pass rate: % of numeric statements that recalc without error.
• Rework rate: % of AI outputs needing major revision.
• Decision speed: Time from AI draft to approved decision (aim to reduce without hurting accuracy).
• Cost avoided: Estimated spend or hours saved by catching a bad claim pre-decision.

Common mistakes and quick fixes

• Mistake: Trusting confident tone. Fix: No action without quotes + dates.
• Mistake: Relying on tertiary sources. Fix: Prioritize primary docs, regulators, publishers of record.
• Mistake: Ignoring time sensitivity. Fix: Enforce a freshness window.
• Mistake: Math looks reasonable but is wrong. Fix: Always run the step-by-step calc prompt.
• Mistake: Cherry-picking favorable evidence. Fix: Force the counter-argument prompt.
• Mistake: Treating opinions as facts. Fix: Label unresolved items as “assumption” and quarantine decisions that depend on them.

Insider trick: Apply the Two-Lens Test on every big claim: Evidence Lens (do we have quotes and dates?) and Incentives Lens (who benefits if this is true, and could that bias the source?). If either lens fails, you don’t ship.

1-week implementation plan

• Day 1: Set your freshness window and impact labels. Save the four prompts above as snippets.
• Day 2: Create a one-page checklist mirroring the 9 steps. Share it with the team.
• Day 3: Audit five recent AI outputs using the prompts. Record pass/fail per claim.
• Day 4: Standardize source tiers (primary/secondary/tertiary) and examples relevant to your industry.
• Day 5: Add a “Verification” section to your report templates: sources, quotes, dates, confidence.
• Day 6: Run a small backtest or pilot on one decision. Track the metrics above.
• Day 7: Review metrics, identify top two failure modes, update the checklist and prompts.

Build the habit: evidence first, math transparent, assumptions explicit, counter-case mandatory. That turns AI from risky guesswork into reliable leverage.

Your move.

[Author]

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