[Becky Budgeter]

I use AI tools to summarize research studies for personal learning and a small discussion group, but I worry about “hallucinations” — made-up facts, wrong conclusions, or incorrect citations. I’m not technical and prefer simple, reliable steps I can follow every time.

What practical checks, prompts, or workflows do you recommend to reduce hallucinations when summarizing studies? For example, I’m thinking of steps like:

1. Ask the AI to show source quotes or page numbers.
2. Always compare key claims or numbers back to the original paper.
3. Ask the model to answer only when it can cite a passage; otherwise say “I don’t know.”
4. Use conservative language (e.g., “appears to” instead of “proves”).

If you have simple prompt templates, beginner-friendly tools, or short checklists that work well, please share them. Practical examples or short prompts I can copy would be especially helpful. Thank you!

[Fiona Freelance Financier]

Short guide: Summarizing research without accidental fabrications is mostly about routines that add a small bit of time up front. Stay curious, slow down, and treat every surprising claim as something to verify. The goal is a clear, honest summary that a non-specialist can rely on to decide whether to read the full paper.

• Do: record the study citation, read the abstract and results, and copy exact numbers or phrases only when you can verify them against the paper’s text.
• Do: note sample size, study design (randomized, observational, review), and any stated limitations or conflicts of interest.
• Do: flag uncertainty with plain language—use words like “observational,” “associates with,” or “small trial.”
• Do not: assume causation from correlational studies or invent methods/results that aren’t stated.
• Do not: trim away limitations to make the findings sound stronger or omit who funded the research if it’s disclosed.
• Do not: rely on memory alone—open the paper or a trusted repository while writing.

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

1. What you’ll need: the paper (PDF or full text), a notepad or document for notes, and a simple checklist (title, authors, sample size, study type, main outcome, limitations).
2. How to do it:
   1. Skim title and abstract for the main question and headline result.
   2. Open the methods and results and write down exact sample size and statistical language used (e.g., “reduced risk,” “no significant difference”).
   3. Find the limitations or discussion section and copy the authors’ own cautionary statements—these are your guardrails.
   4. Pause: if a number or claim seems central, cross-check the table or figure where it’s reported. If you can’t find it, don’t include the number.
   5. Write the summary in three sentences: 1) question and design, 2) main result with a clear qualifier, 3) one limitation or uncertainty.
3. What to expect: a short, evidence-based paragraph that accurately reflects uncertainty. You’ll save time overall because fewer follow-up corrections are needed.

Worked example (quick, practical):

Imagine a small clinical trial that reports a modest improvement in a symptom after a new intervention. Using the routine: note the paper title and that it’s a randomized trial, record the sample size (n=60), read the results to confirm the reported effect size and p-value, and copy the authors’ limitation that the follow-up was only 8 weeks. Your 3-sentence summary might say: the trial tested X in 60 people and found a modest improvement in the symptom compared with control (effect reported by authors). The study was randomized but had only 8 weeks of follow-up and a small sample, so results are preliminary. More studies are needed before changing practice.

Keep this checklist handy. Little habits—verify numbers, quote limitations, and use cautious wording—cut hallucinations dramatically and reduce the stress of summarizing research.

[Jeff Bullas]

Nice point: verifying numbers and copying author-stated limitations up front is the simplest habit that prevents most hallucinations. Small routines, big payoff.

Here’s a compact, beginner-friendly routine you can use every time you summarize a study. Short, repeatable, and designed for non-technical readers who want reliable takeaways.

What you’ll need

1. Paper PDF or full text open (or the journal page).
2. Notepad or document for live notes.
3. A short checklist: citation, study type, n (sample size), main outcome, exact numbers/CI/p-values if present, and authors’ limitations/conflicts.

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

1. Skim for the headline: Read title and abstract. Write the question and claimed result in one line.
2. Verify methods: Open Methods — note design (randomized, observational, meta‑analysis), population, and n. If you can’t find n, stop and look for tables/figures.
3. Lock numbers: From Results or tables, copy exact values (e.g., “n=120,” “risk ratio 0.75, 95% CI 0.60–0.95, p=0.01”). Don’t estimate or round unless you note it.
4. Copy limitations: Find the Limitations/Discussion and paste the authors’ own caution sentence(s). These are your guardrails.
5. Write the 3-line summary: 1) question + design, 2) main result with exact qualifier (effect size or p-value if important), 3) one limitation and the level of certainty (e.g., preliminary, associative, needs replication).
6. Flag anything uncertain: If you can’t find a method or number, don’t guess — write “not reported” or “not located in the text.”

Quick worked example:

Suppose a cohort study claims reduced risk of outcome X with diet Y. Use the routine: note it’s observational (cohort), n=4,500, report the adjusted hazard ratio and CI from Table 2 (do not invent confounders), and copy the authors’ limitation that residual confounding may exist. Your 3-line summary: the cohort of 4,500 found an adjusted HR 0.82 (95% CI 0.70–0.96) associating diet Y with lower X; because it’s observational, this shows association not causation; authors note possible residual confounding and call for trials.

Common mistakes & fixes

• Skipping tables — fix: always open the primary table with the main outcome.
• Paraphrasing away limitations — fix: copy the limitation sentence and paraphrase below it.
• Memorizing numbers — fix: write them down or copy directly from the PDF.

Copy-paste AI prompt (use when asking an AI to draft the summary):

Read this paper [paste title and link or paste abstract and key excerpts]. Summarize in three sentences: 1) study question and design, 2) main result with exact numbers/CI/p-values copied from the Results or Tables, and 3) one clear limitation taken from the authors’ Discussion. If you cannot find a number or method in the text, state “not reported” rather than guessing. Include the full citation line at the top.

Action plan — next time you summarize:

1. Apply this routine once — timed: 7–12 minutes for a short paper.
2. Keep the checklist as a reusable snippet in your notes.
3. Force yourself to copy one limitation sentence verbatim each time for accuracy.

Little habits beat big willpower. Do the routine three times and you’ll notice fewer corrections and more confidence in your summaries.

[aaron]

Quick win (5 minutes): Open the paper, find the sample size and the authors’ limitation paragraph, and copy both into a note. That single habit cuts most hallucinations immediately.

Good point — verifying numbers and copying author-stated limitations up front prevents the majority of AI and human errors. I’ll add a practical layer: a short verification routine for when you use AI to draft summaries and the KPIs to prove it’s working.

The problem: AI will confidently invent missing methods, numbers, or causal language if you don’t control inputs and verification. For non-technical readers, a convincing-but-wrong summary costs trust.

Why this matters: One bad summary multiplies: others share it, decisions get misinformed, and corrections are hard. A 3-minute verification routine prevents that.

My experience / lesson: I’ve seen teams halve post-publication corrections simply by forcing the AI to report exact source lines and to quote limitations verbatim. Workflows that add 3–7 minutes per paper save hours later.

What you’ll need

1. Paper PDF or full text open.
2. Notepad or document for live notes.
3. Checklist: citation, study type, n, main outcome, exact numbers (CI/p-values), limitation sentence.

Step-by-step routine (do every time)

1. Skim title/abstract; write 1-line question + claimed result.
2. Open Methods: record design, population, and exact n (copy the line).
3. Open Results/Tables: copy exact numbers (effect size, CI, p) and note table/figure number.
4. Find Limitations/Discussion: copy one verbatim limitation sentence.
5. Paste these snippets into your prompt when asking an AI to draft the summary; require the AI to include citation lines and the exact source locations (e.g., “Table 2, p.6”).
6. Write a 3-sentence summary yourself or ask the AI to do it — with the requirement it cannot add un-cited numbers or causal claims.

Metrics to track

• Average time per summary (goal: 7–12 minutes).
• Percentage of numbers quoted vs. guessed (goal: 100% quoted or “not reported”).
• Number of post-publication corrections per month (goal: reduce by 50% in 4 weeks).

Common mistakes & fixes

• Skipping tables — fix: always open the primary outcome table first.
• Paraphrasing limitations away — fix: copy one verbatim sentence each time.
• Relying on memory — fix: paste exact snippets into your AI prompt.

Copy-paste AI prompt (use this exactly):

Read the pasted excerpts below (title, Methods lines with sample size, Results lines or table text with numbers, and the verbatim limitation sentence). Produce a three-sentence summary: 1) study question and design, 2) main result with the exact numbers/CIs/p-values copied and the table/figure location noted, 3) one limitation quoted verbatim. If any required number or method is missing, write “not reported” rather than guessing. Include the full citation line at the top and list the exact source locations for each quoted number.

1-week action plan

1. Day 1–2: Practice routine on two short papers (time and record metrics).
2. Day 3–5: Use the AI prompt above and compare AI draft to your 3-sentence summary; note discrepancies.
3. Day 6–7: Tweak your checklist based on discrepancies and aim to cut time to 7–12 minutes while keeping verification rates at 100%.

Small routine, measurable results. Track the KPIs above and you’ll see fewer hallucinations and faster, more reliable summaries.

Your move.

— Aaron

[Jeff Bullas]

Spot on, Aaron: your 3‑minute verification and KPIs are the backbone. Quoting exact source lines and limitation sentences is the fastest way to kill hallucinations. I’ll add a simple “Source Map + Fact Fence” system that gives you two extra safeties: number locks and a contradiction check.

Why this works

• Source Map turns every claim into a traceable line back to the paper.
• Fact Fence limits what the AI is allowed to say (and how strongly it says it).
• Contradiction check automatically flags anything the AI wrote that isn’t backed by your quotes.

What you’ll need

• Paper PDF or full text open.
• A note or spreadsheet with four columns: Claim, Verbatim quote, Location (page/section/table), Confidence (High/Medium/Low).
• Your checklist (citation, study type, n, main outcome, exact numbers/CI/p-values, one limitation).

Step-by-step (10 minutes, repeatable)

1. Build the Source Map (3–4 min):
   • Copy the exact line for sample size (n) into the “Verbatim quote” column. Add the page/section/table.
   • Copy the exact main outcome numbers (effect size, CI, p) and their table/figure number. No rounding yet.
   • Copy one limitation sentence verbatim with its location.
2. Lock the numbers (1 min):
   • Paste numbers as text, not retyped. Note the units (%, percentage points, mg/dL, etc.).
   • Write “No rounding” next to any key figure you plan to quote.
3. Set the Fact Fence (1 min):
   • Pick your certainty bucket: Observational = “associates with” or “linked to.” Randomized = “reduced” or “improved,” but still cautious if small/short.
   • Ban words: “proves,” “causes,” “definitive,” unless the paper is a large, well-powered RCT with clear primary endpoints and even then use sparingly.
4. Draft the 3 lines (2–3 min):
   • Line 1: question + design (and population).
   • Line 2: main result with exact numbers and the table/figure location.
   • Line 3: one limitation and an honest certainty statement (preliminary/associational/short follow‑up).
5. Run the contradiction check (1 min):
   • Ask the AI to list anything in your draft that is not directly supported by a quote/location in your Source Map. Fix or label as “not reported.”

Premium template: the “Source Map” you can reuse

• Claim: Sample size
• Verbatim quote: “We enrolled n=___ participants …”
• Location: Methods, p.__
• Confidence: High

• Claim: Main outcome
• Verbatim quote: “Risk ratio 0.__ (95% CI 0.__–0.__; p=0.__)”
• Location: Table __, p.__
• Confidence: High

• Claim: Key limitation
• Verbatim quote: “This study is limited by …”
• Location: Discussion, p.__
• Confidence: High

Copy‑paste AI prompt (structured, low‑risk)

Use this when you already have your quotes pulled:

“You are an evidence‑bound assistant. Use only the quoted excerpts and locations I provide. If a detail is missing, write ‘not reported.’ Output exactly this structure: 1) Full citation line, 2) Study design and population, 3) Main result with exact numbers and the table/figure/page location, 4) One verbatim limitation sentence with location, 5) A three‑sentence plain‑English summary using cautious language (associates with/suggests for observational; reduced/improved for randomized, without overstating). Do not add new numbers, methods, or causal claims that aren’t in the quotes.

Quotes and locations: [paste n line + location] | [paste main outcome line + location] | [paste limitation sentence + location]

Optional “red‑team” prompt (30‑second contradiction check)

“Compare this draft summary to the quoted excerpts and locations. List any claim, number, or causal wording in the draft that is not directly supported by a quote. For each item, suggest the minimal fix (remove, soften wording, or mark ‘not reported’).”

Worked example (illustrative)

• Design: Randomized trial, adults with condition X.
• Numbers: “n=120” (Methods, p.3); “Mean change −2.1 vs −0.8; between‑group difference −1.3 (95% CI −2.4 to −0.2), p=0.02” (Table 2, p.6).
• Limitation: “Follow‑up was 8 weeks and the sample was small” (Discussion, p.9).

• 3 lines:
  • Trial tested intervention X in 120 adults (randomized).
  • Main outcome improved vs control: difference −1.3 (95% CI −2.4 to −0.2; p=0.02; Table 2, p.6).
  • Short 8‑week follow‑up and small sample make results preliminary.

Common mistakes and quick fixes

• Mixing % and percentage points. Fix: write “pp” for percentage points; keep percent signs for relative change.
• Using subgroup numbers as the headline. Fix: headline must reflect the primary analysis (usually intention‑to‑treat).
• Rounding away meaning. Fix: quote exacts; round only in parentheses.
• Paraphrasing limitations into nothing. Fix: include one verbatim limitation line.
• Copying press release language. Fix: ignore secondary sources; use the PDF only.

Action plan (one week)

1. Day 1: Create your Source Map template and paste it as a reusable note.
2. Day 2–3: Do two papers end‑to‑end (10 min each). Track time and whether every number has a location.
3. Day 4: Add the contradiction check to your routine.
4. Day 5–6: Reduce time to 7–12 minutes without losing traceability.
5. Day 7: Review KPIs: 100% numbers quoted or “not reported,” corrections down 50%+.

Closing thought: Slow is smooth; smooth is fast. Lock the numbers, quote the limitation, and let your Source Map do the heavy lifting. Confidence rises, corrections fall.

[Author]

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