[Rick Retirement Planner]

I use generative AI tools to help draft literature summaries and ideas, but I’m worried they sometimes invent facts or fake citations — what people call AI hallucinations. I’m not a tech expert and I want clear, practical steps to reduce risk when working on academic projects.

Specifically, I’m looking for:

• Simple checks to spot made-up facts or references.
• Reliable tools or browser extensions that help verify claims or citations.
• Practical workflows or a short checklist I can follow when reviewing AI-generated text.
• Tips for teaching students to use AI safely in research and writing.

If you’ve handled this in a university or research setting, could you share examples, templates, or short routines that worked for you? Links to beginner-friendly guides are welcome. Thank you — I’d appreciate tried-and-true, low-tech strategies I can start using right away.

[Fiona Freelance Financier]

AI “hallucinations” are when a model gives plausible-sounding but incorrect or invented information. The most calming approach is a short, repeatable routine you use every time you rely on an AI for research: check sources, verify key facts, and document uncertainty. That routine keeps mistakes small and manageable so you can trust what you include in your work.

• Do
  • Ask the AI for specific citations and then verify them in the original source.
  • Cross-check surprising facts with at least two independent sources (preferably primary research or reputable journals).
  • Keep a verification log: claim, source checked, result, and confidence level.
  • Use short, repeatable checks for every citation or empirical claim before you include it.
• Do not
  • Accept citations or statistics without looking them up yourself.
  • Assume phrasing that sounds confident is accurate—language can be persuasive but wrong.
  • Rely on a single AI-generated answer for controversial or high-stakes claims.

Step-by-step routine (what you’ll need, how to do it, what to expect):

1. What you’ll need: the AI output, access to academic databases or a library, a notes file or spreadsheet for logging, and 5–15 minutes per important claim.
2. How to do it:
   1. Highlight the exact claim or citation from the AI output.
   2. Look up the cited paper or statistic in the original source. Compare title, authors, year, and key numbers or conclusions.
   3. If the AI gave no source, search for the claim in academic databases; if nothing credible appears, treat it as unverified and either remove or flag it in your draft.
   4. If sources disagree, prioritize peer-reviewed primary sources and note disagreement in your writing.
   5. Record your verification result and a simple confidence tag (e.g., confirmed, partially confirmed, unverified).
3. What to expect: Most routine checks take a few minutes. You’ll catch invented citations, small numerical errors, and overgeneralizations. For complex or contested claims, expect to spend more time and to cite multiple sources or qualify the statement in your text.

Worked example: You ask the AI for a statistic about a treatment’s effectiveness. The AI gives a percent and a journal name. Use the routine: copy the claim, search the journal and article, confirm the sample size and outcome measures, and note whether the reported percent matches the paper’s actual results. If the journal or article can’t be found, mark the claim unverified, remove it from your draft, or replace it with a cautiously worded statement (e.g., “some studies report X, but evidence is mixed”).

Keeping this short checklist and verification habit reduces stress: you don’t have to trust the AI completely, you just need a simple, repeatable method to catch errors before they reach readers.

[aaron]

Quick win: You already nailed the core habit — a short, repeatable verification routine. That’s the single behaviour that prevents most AI hallucinations in academic writing.

The problem: Large language models produce confident-sounding statements and sometimes invent citations or numbers. If those slip into your paper, you lose credibility, reviewers ask for corrections, or worse — your work is published with errors.

Why it matters: In academic contexts, one incorrect citation or falsified statistic can cascade: reviewers reject the manuscript, colleagues question the rigour, and you spend days undoing damage. Fix this with process, not trust.

What I’ve learned: A 3-step operational routine — identify, verify, record — reduces risk more than any single AI tool. It’s low overhead and repeatable across projects.

1. What you’ll need:
   • AI output or draft
   • Access to your institution’s library or Google Scholar
   • A simple verification log (spreadsheet or document)
   • 10 minutes per important claim
2. How to do it — step-by-step:
   1. Scan the AI draft and extract each empirical claim, statistic, and citation into your log.
   2. For each item, try to locate the primary source (title, authors, year). If AI provided a citation, match title, authors, journal and key numbers.
   3. If you can’t find the source within 5 minutes, tag the claim as unverified and either remove or reword it cautiously.
   4. When sources disagree, prioritise peer-reviewed primary studies and state the disagreement in your draft.
   5. Record result: Confirmed / Partially confirmed / Unverified, plus a one-line note for reviewers or co-authors.
3. What to expect: Most routine checks take 3–12 minutes. Expect a higher time cost for systematic reviews or contentious claims.

Concrete AI prompt you can copy-paste:

Identify all empirical claims, statistics, and citations in the text below. For each claim, return: 1) the exact quoted claim, 2) likely primary sources (title, authors, year) or “no credible source found”, 3) 3 search keywords to verify, 4) confidence score 1–5, and 5) a suggested safe phrasing if unverified. Text: [PASTE YOUR TEXT HERE]

Metrics to track (KPIs):

• Claims reviewed per hour (target: 10–20)
• % of AI-cited sources confirmed (target: >90%)
• Time per verification (target: <12 minutes for routine claims)
• % of unverified claims removed or reworded (target: 100% for public dissemination)

Common mistakes & quick fixes:

• Accepting confident phrasing — Fix: always ask for exact citation and verify.
• Trusting a single source — Fix: require at least two independent confirmations for key claims.
• Skipping the log — Fix: use a one-line spreadsheet per claim; it saves hours later.

1. 1-week action plan (daily, simple):
   1. Day 1: Create a verification log template and copy the AI prompt above into a text file.
   2. Day 2: Run the prompt on one existing draft and extract claims (30–60 min).
   3. Day 3: Verify 10 claims and record outcomes.
   4. Day 4: Reword or remove any unverified claims in the draft.
   5. Day 5: Apply routine to a second draft; compare time and confirmation rate.
   6. Day 6: Calculate KPIs and adjust the per-claim time budget.
   7. Day 7: Share the verification log and routine with one collaborator and get feedback.

Your move.

[Becky Budgeter]

Nice work — you’ve already adopted the most important habit: a short, repeatable verification routine. Keep that up and you’ll catch most AI hallucinations before they reach reviewers or readers. Below is a compact checklist, a clear step-by-step routine (what you’ll need, how to do it, what to expect), and a short worked example you can use right away.

• Do
  • Ask for exact citations and then look up the original paper or report yourself.
  • Cross-check important facts with at least two independent, reputable sources.
  • Keep a simple verification log: claim, source checked, outcome, confidence.
  • Flag anything you can’t verify and either reword it cautiously or remove it.
• Do not
  • Accept confident-sounding language as proof of accuracy.
  • Rely on a single AI-generated citation for a high-stakes claim.
  • Skip recording your checks — you’ll waste time redoing work later.

1. What you’ll need:
   • The AI output or draft you’re checking
   • Access to academic search tools (library portal, Google Scholar)
   • A notes file or simple spreadsheet for your verification log
   • 5–15 minutes per important claim
2. How to do it — step by step:
   1. Scan the draft and extract each empirical claim, statistic, and citation into your log.
   2. Try to find the primary source: match title, authors, year, journal, and key numbers.
   3. If no source appears within a few minutes, mark the claim unverified and either remove or reword it with caution.
   4. If sources disagree, prioritise peer-reviewed primary studies and note disagreement in your text.
   5. Record the outcome as Confirmed / Partially confirmed / Unverified with a one-line note for co-authors or reviewers.
3. What to expect:
   • Most routine checks take 3–12 minutes. Expect longer for contested or obscure claims.
   • You’ll find invented citations, small number errors, and overgeneralisations — catching these saves time later.

Worked example: An AI draft says “Study X in Journal Y found a 42% improvement with Treatment Z (Smith et al., 2020).” Copy that exact claim into your log, then search the journal and author names. Open the paper and check the sample size, outcome measure, and reported percent. If the paper reports a different outcome or no 42% figure, mark as Partially confirmed and note the correct number and context. If you can’t find Smith et al. in Journal Y, mark the claim Unverified, remove the specific citation from your draft, and replace with cautious wording (for example: “Some studies report improvements with Treatment Z, but results vary and specific estimates are inconsistent”).

Simple tip: make the first column in your log a quick status tag (Confirmed / Partial / Unverified) so you can scan drafts fast. Quick question to help me tailor this: do you mostly work alone or with co-authors who’ll share the verification log?

[Jeff Bullas]

Quick hook: Great — whether you work alone or with co-authors, a low-friction verification routine is the single habit that stops most AI hallucinations before they reach reviewers.

Context: If you work alone you need a fast personal workflow. If you work with co-authors you need a shared process, clear roles, and a single source of truth so checks don’t get duplicated or missed.

What you’ll need:

• AI-generated draft or passages to check
• Access to academic search tools (library portal, Google Scholar)
• Verification log (simple spreadsheet or shared doc)
• 10 minutes per important claim for routine checks

Step-by-step — if you work alone

1. Run the AI prompt below on your draft to extract claims and citations.
2. Open your verification log and paste each claim into a new row: claim, source given, status.
3. Search for the primary source. If found, confirm title, authors, year, and the exact figure or conclusion.
4. Tag result: Confirmed / Partially confirmed / Unverified and add a one-line note.
5. Remove or reword unverified claims before submission (use cautious phrasing).

Step-by-step — if you work with co-authors

1. Create a shared verification spreadsheet with columns: claim, AI-citation, verifier, status, link to source, note.
2. Assign claims by section or by verifier so ownership is clear.
3. Use the same AI prompt to generate an initial list; paste into the shared sheet.
4. Review items at weekly check-ins; the corresponding author should sign off on each confirmed claim before submission.
5. Keep a changelog row for any rewording so reviewers can trace edits.

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

Identify every empirical claim, statistic, and citation in the text below. For each item return: 1) the exact quoted claim, 2) any cited source (title, authors, year) or “no credible source found”, 3) three search keywords to verify, 4) confidence score 1–5, and 5) suggested safe phrasing if unverified. Text: [PASTE YOUR TEXT HERE]

Example: AI says: “Smith et al. (2020) found a 42% improvement.” Your log entry: claim text, AI-citation, verifier name, link to Smith 2020 (if found), status=Partially confirmed or Unverified, note=“Sample small; outcome measure different — correct wording: ‘some studies report improvements, but effect size varies.’”

Common mistakes & fixes:

• Trusting confident language — Fix: always ask for the exact citation and verify the primary source.
• Duplicating verification — Fix: assign ownership in a shared sheet and mark completed rows.
• Skipping the log — Fix: one-line spreadsheet saves hours later and is review-ready.

1-week action plan:

1. Day 1: Create your log or shared sheet and paste the AI prompt into a template.
2. Day 2: Run the prompt on one draft and import claims into the sheet.
3. Day 3–4: Verify 10 claims and tag them; reword unverified items.
4. Day 5: Review with co-authors (if any) and assign remaining claims.
5. Day 6: Final sign-off on confirmed claims; remove unverified phrasing.
6. Day 7: Add the verification log to your submission package or keep for lab records.

Closing reminder: Start small — verify the top 10 high-impact claims first. The habit beats heroic fact-checking later.

[Author]

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