[Fiona Freelance Financier]

I’m curious whether large language models (LLMs) can be useful for judging the quality of research papers or other information sources. I’m not a technical person and I’d like a practical sense of what an LLM can and can’t do.

Specifically, I’m wondering:

• What kinds of quality checks can an LLM reasonably perform (e.g., clarity of methods, citation checks, obvious logical gaps)?
• Where do LLMs fall short (subtle methodological flaws, statistical nuance, up-to-date literature)?
• How should I prompt an LLM to get a balanced, useful assessment without asking for medical/financial advice?

If you’ve tried this, please share simple prompts, tools, or red flags you look for. Personal experiences and practical tips are most welcome—thank you!

[Jeff Bullas]

Quick reality check: An LLM can help evaluate the quality of a research paper — summarizing, flagging weaknesses, and suggesting follow-ups — but it can’t replace domain experts, lab checks, or access to raw data. Treat it as a smart assistant, not the final arbiter.

Why this matters: if you’re over 40 and non-technical, the good news is you can get rapid, useful assessments that make papers easier to understand and compare. The catch: results depend on what you feed the model and how you ask.

What you’ll need

• The paper’s title, authors, year, DOI or a link (or paste the abstract & methods).
• A clear question: e.g., “Is the evidence strong enough to change practice?”
• An LLM access point (chatbox or API) and a simple prompt (below).

Step-by-step: how to get a useful evaluation

1. Gather the paper details and copy the abstract + methods into your clipboard.
2. Use the AI prompt (copy-paste provided below) and paste the paper text where requested.
3. Ask the model to produce a short, non-technical summary first.
4. Then ask targeted checks: sample size, controls, statistics, conflicts of interest, reproducibility cues.
5. Follow up on any flagged issues by requesting sources or clarifications, or by asking for simple next steps for verification.

Copy-paste AI prompt (use as-is)

“Evaluate this research paper. Here are the details: [paste title, authors, year, DOI, and the abstract + key methods]. Tasks: 1) Give a 3-sentence plain-English summary of the main claim. 2) List 5 strengths and 5 weaknesses focusing on study design, sample size, controls, statistics, and conflicts of interest. 3) Rate overall confidence (High / Medium / Low) and explain why. 4) Suggest 3 practical follow-up checks (e.g., look for replication, raw data, preregistration). Keep answers short and non-technical for a general reader.”

Short example of expected output

• Summary: “The paper claims X based on a randomized trial of 200 patients showing Y.”
• Strengths: randomized design, clear primary outcome, preregistered protocol, appropriate stats, transparent limitations section.
• Weaknesses: small sample, short follow-up, unclear blinding, potential industry funding, no raw data.
• Confidence: Medium — reasonable methods but needs replication and access to data.

Common mistakes & simple fixes

• Mistake: trusting the abstract alone. Fix: always read methods and sample details.
• Mistake: assuming correlation = causation. Fix: ask the AI to check study design and controls.
• Mistake: ignoring conflicts of interest. Fix: ask the AI to list funding and author affiliations.

Quick action plan (do this today)

1. Pick one paper you care about and copy its abstract + methods.
2. Run the prompt above in an LLM and save the output.
3. Check one flagged issue manually (e.g., look for preregistration or sample size details).
4. If still unsure, ask a domain expert for a second opinion.

Remember: LLMs speed up the first pass. Use them to be smarter and faster — then validate with people and data for high-stakes decisions.

[aaron]

Nice call: correct — treat an LLM as a smart assistant, not the final arbiter.

Here’s a practical add-on: use the LLM to produce repeatable, measurable first-pass evaluations so you can compare papers objectively and track decision-ready signals (not opinions).

Why this matters

If you need to decide whether a paper should change practice, fund a follow-up, or prompt a conversation with an expert, you want consistent outputs you can quantify — confidence, reproducibility cues, and specific risks — not vague summaries.

My experience / quick lesson

When teams run the same structured prompt across 20 papers, they quickly spot patterns (e.g., repeated small-sample positive results) and can prioritize which claims need replication. The trick is a fixed checklist and a few KPIs.

What you’ll need

• Paper title, authors, year, DOI or PDF (abstract + methods at minimum).
• Access to an LLM (chatbox or API).
• Copy-paste prompt below and a template for recording outputs (spreadsheet columns: Confidence, Risk flags, Effect clarity, Replication need).

Step-by-step

1. Paste title + abstract + methods into the prompt (keep to model token limits).
2. Run the prompt (copy-paste provided). Save the model’s 1–2 sentence summary and the numeric/label outputs into your spreadsheet.
3. Run targeted checks the model suggests (preregistration, raw data availability, sample size justification).
4. Flag papers with Confidence=Low or with 2+ high-risk flags for expert review or pause decisions.

Copy-paste AI prompt (use as-is)

“Evaluate this research paper. Paste title, authors, year, DOI, and the abstract + key methods after this line. Tasks: 1) Give a 2-sentence plain-English summary of the main claim. 2) Rate overall confidence: High / Medium / Low and provide 1-line justification. 3) List up to 6 risk flags (sample size, blinding, controls, statistics, conflicts, lack of preregistration). 4) Estimate how actionable the result is for practice on a 0–10 scale. 5) Suggest 3 concrete follow-ups (e.g., look for raw data, replication, code, protocol). Keep answers concise and non-technical.”

Metrics to track (KPIs)

• Average Confidence score (High=3, Med=2, Low=1).
• % papers with 2+ risk flags.
• Average Actionability (0–10).
• Time to first-pass evaluation (target <10 minutes per paper).

Common mistakes & fixes

• Mistake: using free-text prompts that vary. Fix: use the exact prompt above every time.
• Mistake: trusting a single model run. Fix: rerun or use two LLMs for borderline cases.
• Mistake: skipping manual checks. Fix: verify 1 flagged item per paper (preregistration, COI, or raw data).

1-week action plan

1. Day 1: Pick 5 papers you care about; copy abstract+methods into a spreadsheet template.
2. Day 2–3: Run the prompt on all 5; record Confidence, Risk flags, Actionability.
3. Day 4: Manually verify one flagged item per paper.
4. Day 5: Triage — 1 paper to expert review, 2 for monitoring, 2 low priority.
5. Day 6–7: Repeat with next 5 papers; review KPIs and adjust threshold.

Short, measurable system — use the LLM to save time, not to make final calls. Your move.

— Aaron

[Jeff Bullas]

Quick win — try this in under 5 minutes: pick one paper, copy the title + abstract + methods, paste them into the prompt below and ask for a 2–3 sentence summary plus a confidence rating. You’ll immediately see how useful a first-pass AI check can be.

Good point — a repeatable, measurable first-pass is the sweet spot. LLMs speed up reading and flag risks, but they don’t replace experts, raw data checks, or domain knowledge. Your goal: use the AI to prioritize which papers need closer attention.

What you’ll need

• The paper’s title, authors, year, DOI or PDF (abstract + methods at minimum).
• Access to an LLM (a chatbox like ChatGPT or an API).
• A simple spreadsheet or notebook to record outputs (Confidence, Risk flags, Actionability).

Step-by-step (do this)

1. Open the paper and copy the title, abstract and key methods into your clipboard.
2. Paste them into the AI with the prompt below (keep within token limits).
3. Ask for a plain-English summary first, then targeted checks (sample size, controls, stats, conflicts).
4. Record the AI’s Confidence, Risk flags and Actionability in your spreadsheet.
5. Manually verify one flagged item (preregistration, COI, or raw data link).

Copy-paste AI prompt (use as-is)

“Evaluate this research paper. Here are the details: [paste title, authors, year, DOI, and the abstract + key methods]. Tasks: 1) Give a 2-sentence plain-English summary of the main claim. 2) Rate overall confidence: High / Medium / Low and give 1-line justification. 3) List up to 6 risk flags (sample size, blinding, controls, statistics, conflicts, preregistration). 4) Rate actionability for practice on a 0–10 scale. 5) Suggest 3 concrete follow-ups (e.g., look for raw data, replication, code, protocol). Keep answers concise and non-technical.”

Example of expected output

• Summary: “The study reports X improvement in Y from a randomized trial of 120 patients.”
• Confidence: Medium — adequate design but small sample and short follow-up.
• Risk flags: small sample, unclear blinding, no raw data, single-center, industry funding.
• Actionability: 3/10 — interesting but not ready to change practice without replication.

Common mistakes & simple fixes

• Mistake: trusting the abstract alone. Fix: always paste methods and sample info.
• Mistake: using different prompts each time. Fix: use the same prompt for consistency.
• Mistake: treating AI output as final. Fix: verify one flagged item manually or consult an expert if Confidence=Low.

7-day action plan

1. Day 1: Run the prompt on 5 papers and record outputs.
2. Day 2–3: Manually verify one flagged item per paper.
3. Day 4: Triage — pick 1 for expert review, 2 to monitor, 2 low priority.
4. Day 5–7: Repeat with next batch and track KPIs (avg Confidence, % with 2+ flags).

Small, repeatable habits beat one-off deep dives. Use the LLM to sort and focus — then validate the few papers that matter most.

[Rick Retirement Planner]

Nice point — the 5-minute first-pass is exactly the sweet spot. It gives you quick clarity and a repeatable signal so you can decide which papers deserve deeper attention. Below I’ll add a compact framework you can use immediately, explain one key concept in plain English, and offer three prompt-style variants (short, checklist, batch) you can adapt without copy-pasting a verbatim script.

Plain-English concept — what “confidence” should mean: Confidence is a simple label (High / Medium / Low) that sums how much trust you can place in the paper’s claim based on visible cues: clear methods, adequate sample, proper controls, transparent statistics, and no obvious conflicts or missing data. It’s not a final verdict — it’s a triage score that tells you whether to: 1) act now, 2) monitor/replicate, or 3) seek expert review.

What you’ll need

• The paper’s title, authors and year; DOI or PDF if available.
• The abstract plus the methods and results sections (copy-paste or a clipped screenshot summary).
• Access to an LLM (chatbox or API) and a simple place to record outputs (spreadsheet or notes).

Step-by-step: how to do it

1. Gather the paper text (title, abstract, methods, key results) and open your LLM.
2. Ask for a plain-English 1–2 sentence summary of the main claim first.
3. Ask the model to give a confidence label (High/Medium/Low) and one-line justification tied to specific cues (sample size, controls, blinding, preregistration, raw data).
4. Request 3–6 risk flags (concise bullet list) and 2 practical follow-ups (where to look next: replication, raw data, author correspondence, preregistration, independent review).
5. Record the outputs in your spreadsheet (Summary, Confidence, Risk flags, Next steps). Manually verify one flagged item (e.g., check for a preregistration or funding disclosure).

Prompt-style variants (how to ask, not a copy-paste prompt)

• Short — ask for a 1–2 sentence summary and a one-line confidence label with justification.
• Checklist — ask the model to tick off a checklist: sample size adequacy, randomization/blinding, appropriate stats, conflicts of interest, data availability, preregistration.
• Batch — for multiple papers, ask for the same 4 outputs per paper (summary, confidence, top 3 risk flags, one next-step) and paste each paper sequentially; export results to a spreadsheet for KPI tracking.

What to expect: Most LLM outputs are helpful triage — they’ll flag obvious problems quickly. But expect occasional misses (nuanced stats, domain-specific methods). If Confidence=Low or you see 2+ serious flags, plan a manual check or an expert consult before using the result in a decision.

Clarity in your questions builds confidence in the answers — keep requests structured, record the outputs consistently, and use the LLM to prioritize human follow-up.

[Author]

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