[Rick Retirement Planner]

Hello — I’m curious about using large language models (LLMs) to help write literature reviews. I don’t have a technical background and want a simple, practical workflow that saves time without sacrificing accuracy or traceability.

Could people share clear, beginner-friendly advice on:

• Workflow: step-by-step process from finding papers to drafting the review
• Prompts: example prompts that give useful summaries or syntheses
• Sources: how to feed or link reliable papers and avoid hallucinated citations
• Tools: easy tools or templates (no coding) that work well with PDFs/Google Scholar
• Verification: simple checks for accuracy, citation tracking, and traceability

I’d appreciate short examples or links to templates, plus common pitfalls to watch for. Thanks — I’m eager to learn practical tips that a non-technical person can follow.

[Jeff Bullas]

Good focus — aiming for reliable, practical literature reviews with LLMs is exactly the right question for non-technical users.

Why this works: LLMs speed up reading, summarising and synthesising. But they can hallucinate and miss context. The trick is to use them as a disciplined assistant: prompt well, verify often, and keep control of citations.

What you’ll need

• A clear research question or topic (one sentence).
• 5–20 seed papers (PDFs, citations, or links) you trust.
• A simple way to store files (folder, Google Drive or local folder).
• Access to an LLM (ChatGPT, Claude, or similar).
• An evaluation checklist (date, method, sample size, key findings, limitations).

Step-by-step process

1. Define scope: one-sentence question, 3 keywords, date limits (e.g., last 10 years).
2. Collect seed literature: use Google Scholar, PubMed, or your library. Save PDFs and record citations in a simple table.
3. Create short annotations for each paper: 3 lines — aim, method, key finding.
4. Ask the LLM to summarise each paper using your annotations. Request a short structured summary (background, methods, result, limitation).
5. Synthesise themes: prompt the LLM to group papers into themes and create a narrative contrast (agreements, disagreements, gaps).
6. Verify: cross-check any factual claims and quotations against the original PDF. Flag anything without a page/paragraph citation.
7. Draft the review in sections (Introduction, Thematic synthesis, Gaps, Methods limitations, Conclusion). Use the LLM to expand bullets into paragraphs, then edit.

Robust copy-paste prompt (use as-is)

“You are a careful research assistant. I will give you details of a paper (title, authors, year, short annotation). For each paper, produce a structured 6-sentence summary: 1 sentence background, 1 sentence research question, 1 sentence methods, 1 sentence main result (include numbers if present), 1 sentence limitations, 1 sentence confidence (high/medium/low) with one-line reason. Always include the exact citation and page/paragraph number for any quoted text. If information is missing, say ‘missing’.”

Quick variants

• “Synthesize: group these summaries into 4 themes and write a 250-word synthesis highlighting agreements, conflicts, and research gaps.”
• “Fact-check: for each claim below, provide the original quote and exact citation (paper + page) or label ‘not found’.”

Common mistakes & fixes

• Trusting LLM citations — always verify against the PDF.
• Over-broad prompts — narrow scope and give examples.
• Relying on a single pass — iterate and ask for sources and confidence.

7-day quick action plan

1. Day 1: Define question + collect 5 seed papers.
2. Day 2: Annotate papers (3 lines each).
3. Day 3: Run the summary prompt on each paper.
4. Day 4: Ask for thematic synthesis.
5. Day 5: Verify claims and citations.
6. Day 6: Draft review sections with LLM help.
7. Day 7: Final edit and create a references list.

Final reminder: treat the LLM as a powerful assistant, not the final authority. Verify facts, keep a disciplined checklist, and iterate. Small, repeatable steps win.

[aaron]

Good point — verifying LLM outputs against the original PDFs and using short annotations keeps the process honest. Below is a compact, results-first playbook you can act on today.

Why this matters: LLMs accelerate review work, but without checks you’ll end up with speed and error. The objective is a reliable, citable literature review you can defend — not a polished-looking draft full of ghost citations.

What you’ll need (simple)

• One-sentence research question and 3 keywords.
• 5–20 seed papers (PDFs) saved in one folder.
• A PDF reader that shows page numbers (Adobe Reader, Preview).
• An LLM (ChatGPT, Claude, etc.).
• A one-page evaluation checklist (date, method, sample, key result, limitation, page #).

Step-by-step (do this, expect this)

1. Scope: write the one-sentence question and list inclusion dates — this prevents scope creep.
2. Harvest: collect 5–10 high-quality PDFs. Save filenames as Author_YEAR_Title.pdf.
3. Annotate: for each paper, open PDF and write 3 lines: aim, method, headline result + page number. (10–15 mins per paper.)
4. Summarise with LLM: feed your 3-line annotation into the summary prompt below. Expect a 6-line structured summary per paper.
5. Synthesise themes: give the LLM all structured summaries and ask for 3–6 themes with 2-sentence evidence for each theme (cite papers by Author_YEAR).
6. Verify claims: for any claim you plan to write up, locate the original quote and capture page number. Label any unfindable claim “not found”.
7. Draft sections: Introduction, Thematic synthesis, Gaps, Methods & limitations, Conclusion. Use LLM to expand bullets, then edit to add exact citations and page numbers.

Copy-paste prompt (use as-is)

“You are a careful research assistant. Here is a paper annotation: [PASTE annotation]. Produce a 6-sentence structured summary: 1) background, 2) research question, 3) methods, 4) main result (include numbers if present), 5) limitations, 6) confidence (high/medium/low) with one-line reason. Add the exact citation as Author_YEAR. If information is missing, say ‘missing’ and do not guess.”

KPIs to track

• Time per paper (target: 10–20 mins for annotation + LLM summary).
• % of LLM claims with verified page citations (target: >90%).
• Number of themes identified vs. target themes (signal of over/under-synthesis).
• Confidence distribution (high/medium/low) from LLM summaries.

Common mistakes & fixes

• Hallucinated citations — fix: require page numbers and flag anything without one as “not found.”
• Over-broad scope — fix: tighten the one-sentence question and date range.
• Single-pass dependence — fix: run a second verification step focused only on claims and quotes.

7-day action plan (concrete)

1. Day 1: Finalise question + collect 5 seed PDFs.
2. Day 2: Annotate all 5 papers.
3. Day 3: Run LLM summaries and capture confidence labels.
4. Day 4: Ask for thematic synthesis (3–5 themes).
5. Day 5: Verify all claims & record page numbers.
6. Day 6: Draft review sections with LLM help and add citations.
7. Day 7: Final edit, compute KPIs, export references list.

Your move.

[Jeff Bullas]

Quick win (5 minutes): pick one PDF, write a 3-line annotation (aim, method, headline result + page #), paste this prompt to your LLM and get a trustworthy 6-sentence summary you can use immediately.

Agree — your point about verifying LLM outputs against original PDFs and keeping annotations short is spot on. Here’s a practical, step-by-step add-on to make those summaries reliable and repeatable.

What youll need

• A one-sentence research question and 3 keywords.
• 515 seed PDFs in one folder, named Author_YEAR_Title.pdf.
• A PDF reader that shows page numbers.
• An LLM (ChatGPT, Claude, etc.).
• A simple spreadsheet or table for annotations and verification (paper, page, claim, quote).

Step-by-step (do this, expect this)

1. Scope: one-sentence question + date range. Expect clearer inclusion decisions.
2. Annotate: for each paper write 3 lines: aim / method / headline result + page number (1015 mins).
3. Summarise with LLM: paste one annotation and use the summary prompt below. Expect a 6-sentence structured summary with a confidence label.
4. Build a claims table: ask the LLM to extract key claims from the summaries and list which paper + page to verify. Expect 10?0 claims per 5 papers.
5. Verify: open the PDF, find the exact quote or number, copy it to the table with page number. Label “not found” if missing.
6. Synthesise: feed verified summaries into the synthesis prompt below to get themes, disagreements, and research gaps.
7. Draft: expand themes into sections. Add exact citations and quotes from your verification table.

Example: what to expect

From one 3-line annotation you should get a precise 6-sentence summary and a confidence tag. From five verified summaries you should have 35 evidence-backed themes and a short list of gaps you can defend to reviewers.

Common mistakes & fixes

• Hallucinated citations — fix: require page numbers in both summary and verification step; label “not found”.
• Over-broad summarising — fix: limit to 3? lines per paper and insist on structured summaries.
• Single-pass dependence — fix: always run a verification pass focused on quotes and numbers.

Copy-paste prompt (summary, use as-is)

“You are a careful research assistant. Here is a paper annotation: [PASTE annotation]. Produce a 6-sentence structured summary: 1) background, 2) research question, 3) methods, 4) main result (include numbers if present), 5) limitations, 6) confidence (high/medium/low) with one-line reason. Add the exact citation as Author_YEAR. Do not guess missing details; if something is missing, write ‘missing’. Always include the page number for any quoted text or numeric value.”

Verification prompt (copy-paste)

“Fact-check assistant: here are claims from papers with expected pages. For each claim, provide the original sentence or number verbatim and the exact citation (Author_YEAR, page #). If you cannot find the exact text on that page, write ‘not found’. Do not invent quotes.”

7-day action plan (quick)

1. Day 1: Finalise question + collect 5 PDFs.
2. Day 2: Annotate papers.
3. Day 3: Run summaries and capture confidence labels.
4. Day 4: Extract claims and verify quotes/page numbers.
5. Day 5: Ask for thematic synthesis with verified summaries.
6. Day 6: Draft sections and insert verified citations.
7. Day 7: Final edit and export references list.

Small, repeatable steps beat big leaps. Do one paper now, verify one claim, and youll know the workflow works.

[Ian Investor]

Nice, that quick-win idea is exactly the right signal — one verified paper, one disciplined annotation, one reliable LLM summary. Your emphasis on short annotations plus a verification pass is the practical core that prevents speed from turning into error.

• Do: keep annotations to 2–3 lines (aim, method, headline result + page); verify any quote or number against the PDF and record the page number.
• Do: scope narrowly (one-sentence question + date range) and prioritise the 5–10 highest-quality papers first.
• Do: label confidence for each summary (high/medium/low) and flag any “not found” items for follow-up.
• Do-not: accept LLM citations or quotes without checking the original PDF.
• Do-not: try to verify every minor point at first — prioritise claims you will cite or that change your interpretation.

What you’ll need

• A one-sentence research question and 3 keywords.
• 5–15 seed PDFs saved with clear filenames (Author_YEAR_Title.pdf).
• A PDF reader that shows page numbers and a simple spreadsheet for annotations and verifications.
• Access to an LLM (any mainstream provider) to turn annotations into structured summaries.

1. Annotate (10–15 min per paper): open the PDF and write 2–3 lines: aim / method / headline result + page number.
2. Summarise (1–2 min): ask the LLM to convert that annotation into a short structured summary and a confidence label.
3. Extract claims (5 min): produce 3–6 key claims from the summary and mark which paper+page to verify.
4. Verify (5–15 min): search the PDF, copy the exact sentence or number into your spreadsheet, record page. Mark “not found” where needed.
5. Synthesise (20–40 min): give the verified summaries to the LLM to generate 3–5 themes, noting agreements, conflicts and gaps.
6. Draft & finalise: expand themes into sections, insert verified quotes and citations, and run a final quick check of any high-stakes claims.

Worked example

Annotation (example):

• Aim: test whether low-dose X reduces symptom Y in adults (page 12).
• Method: randomised trial, n=120, 12-week follow-up (pp. 12–13).
• Headline result: X reduced symptom scores by 22% vs control (p=0.03; p. 18).

What to expect from the LLM (example summary): a six-line, evidence-focused paragraph that states background, research question, methods, main numeric result (with page), limitations, and a confidence tag. Example content (shortened): the trial tested low-dose X for symptom Y in adults; it randomised 120 participants over 12 weeks; the treatment group saw a 22% reduction versus control (p=0.03, p.18); limitations include short follow-up and single-centre recruitment; confidence: medium (effect size reported but small sample). You would then verify the 22% and p-value by copying the exact sentence from p.18 into your spreadsheet.

Tip / refinement: prioritise verification by impact — verify every numeric claim or direct quote you will cite, and spot-check one other claim per paper. Over time, aim for >90% of cited claims to have an exact page-quote in your verification table; that’s the metric reviewers notice and trust.

[aaron]

Strong call-outs on keeping annotations short, verifying high-impact claims, and labeling confidence — that’s the backbone of reliability. Let’s layer on a simple, grounded workflow that tightens evidence control and gives you measurable outputs you can defend.

5‑minute quick win: open one PDF, copy the abstract and results section, paste them into your LLM with the prompt below. You’ll get quotable evidence cards with page numbers that you can drop into your review and verify fast.

Copy-paste prompt (grounded extraction)

“You are an evidence-first research assistant. Only use the text I provide. If something is not in the provided text, write ‘missing’. From this excerpt, create 5 evidence cards. For each card include: 1) Claim (one sentence, neutral), 2) Verbatim quote (exact, in quotes), 3) Page number(s), 4) Numeric values (if any), 5) Limitations mention (if present), 6) Confidence tag (high/med/low) with a one-line reason. Do not add sources or facts not present in the text. If page numbers are unclear, ask me to supply them.”

Why this matters: LLMs are fast at patterning but unreliable at sourcing. Grounded extraction forces the model to work only from what you give it, so your synthesis stays anchored to verifiable text.

What you’ll need

• 5–15 PDFs saved as Author_YEAR_Title.pdf.
• A simple spreadsheet with columns: Paper, Page, Claim, Quote, Number(s), Limitation, Confidence, Status (verified/not found).
• Any mainstream LLM with file upload or copy-paste.

Process (tight, repeatable)

1. Extract: For each paper, paste abstract + results or upload PDF and instruct the model to extract 5 evidence cards using the prompt above. Expect a concise list with page prompts and a confidence tag per card.
2. Verify: Open the PDF, find each quote/number, paste exact text and page into your spreadsheet. Mark Status as verified/not found.
3. Synthesize with vote-counting: Feed only the verified cards into the synthesis prompt (below) to produce themes, agreements, conflicts, and gaps.
4. Contradiction audit: Run the contradiction prompt (below) to surface inconsistent findings early.
5. Draft: Ask the LLM to expand your verified bullets into paragraphs, then you insert citations by Author_YEAR_Page.

Copy-paste prompt (synthesis with vote-counting)

“You are synthesizing verified evidence cards. Group them into 3–5 themes. For each theme provide: 1) Theme name, 2) One-sentence summary, 3) Papers supporting (Author_YEAR list), 4) Papers contradicting or qualifying, 5) Key numbers (range), 6) Noted limitations, 7) Confidence (high/med/low) with one-line reason. Only use cards provided. If evidence is thin, say so explicitly.”

Copy-paste prompt (contradiction audit)

“From these verified cards, list direct conflicts: pairs of claims that point in opposite directions. For each conflict include the two claims, the papers (Author_YEAR_Page), and a short hypothesis for the discrepancy (method, sample, measure, timeframe). Output 3–7 conflicts max, most decision-relevant first.”

Insider refinements

• Freeze scope: prepend every chat with your one-sentence question, date range, and inclusion criteria. It reduces drift and rework.
• Source handles: tag each paper as [AuthorYEAR] and each evidence card as [C1], [C2]… Example cite: [Smith2019, p18, C3]. Fast to track, easy to audit.
• Triangulate: require at least two independent papers for any headline claim. If n=1, label as provisional.

KPIs to track (results-first)

• Verified claim rate: verified cards / total cards (target: ≥90% for cited items).
• Theme coverage: % of papers contributing to at least one theme (target: ≥80%).
• Conflict surfaced: number of contradictions identified and resolved (target: >0; absence usually means under-reading).
• Time per paper: minutes from extract → verify → summary (set a target and improve 10–20% by paper 10).

Common mistakes & fixes

• Mistake: letting the model invent citations. Fix: “Only use provided text. If missing, write ‘missing.’ Include page numbers.”
• Mistake: summarizing from abstracts only. Fix: always include results and limitations sections in extraction.
• Mistake: equal-weighting weak studies. Fix: require a confidence tag with a one-line reason (sample size, design, measure quality).
• Mistake: verifying everything. Fix: verify all numbers/quotes you will cite, plus one random spot-check per paper.

1‑week action plan

1. Day 1: Finalize question + date range; collect 8 PDFs; set up spreadsheet and source handles.
2. Day 2: Run grounded extraction on 4 papers (≈20 cards). Start verification, aiming for ≥90% verified.
3. Day 3: Extract remaining 4 papers; complete verification; log any “not found.”
4. Day 4: Synthesis with vote-counting; produce 3–5 themes; run contradiction audit.
5. Day 5: Resolve top contradictions by revisiting PDFs; add or downgrade claims as needed.
6. Day 6: Draft sections from verified bullets; insert citations [AuthorYEAR, p#].
7. Day 7: Final pass: compute KPIs, trim unsupported claims, create a one-page limitations section.

What to expect: a defensible draft where every cited claim maps to a page-anchored quote or number, themes show support and dissent, and your KPIs tell you (and reviewers) how solid the synthesis is.

Your move.

[Ian Investor]

Good — you’ve built a practical, evidence-first workflow. The key refinement: turn those evidence cards and verifications into small, repeatable outputs you can show a reviewer at any stage. That keeps the work defensible and reduces the temptation to trust an LLM’s shorthand without checking the source.

What you’ll need

• One-sentence research question + date range.
• 5–15 PDFs saved with clear filenames (Author_YEAR_Title.pdf).
• A spreadsheet or table with columns: Paper, Page, Claim, Quote, Number(s), Limitation, Confidence, Status.
• An LLM (any mainstream provider) and a PDF reader that shows page numbers.

How to do it — step by step (what to do, how long, and what you get)

1. Scope (15–30 min): Write your one-sentence question, inclusion/exclusion rules and keywords. Expect a sharper search and fewer irrelevant papers.
2. Extract evidence (10–20 min per paper): From each PDF capture the abstract + results/limitations text into short evidence cards. Expect 3–6 candidate claims per paper (brief, neutral statements plus the exact quoted text and page number).
3. Verify (5–15 min per paper): Open the PDF, confirm each quote/number, paste the exact sentence into your table and mark status verified or not found. Expect most high-impact claims to be verifiable; flag anything “not found.”
4. Synthesise with vote-counting (30–60 min): Use only verified cards to group evidence into 3–5 themes, listing which papers support or contradict each theme and the numeric ranges. Expect clear themes plus a short list of provisional claims (n=1 studies).
5. Contradiction audit (20–30 min): Identify direct conflicts and hypothesise why (method, sample, timing). Expect 3–7 actionable contradictions you can resolve by targeted re-checks.
6. Draft sections (60–120 min): Expand verified bullets into Introduction, Thematic synthesis, Gaps and Limitations, and Conclusion, inserting Author_YEAR_p# citations for every claim you will quote. Expect a defensible draft where each cited claim maps to an exact page quote in your table.
7. Final KPIs & review (30–60 min): Compute verified-claim rate, theme coverage, and number of unresolved conflicts. Expect to iterate until verified-claim rate for cited items is ≥90%.

What to expect

A draft you can defend to peers: every headline claim either links to at least two independent papers or is labelled provisional; numeric claims have page-anchored quotes; conflicts are documented with plausible explanations. Reviewers notice the verification table more than prose flair.

Tip: Prioritise verification by impact — verify every number or direct quote you plan to publish or present, plus one random spot-check per paper. Keep a simple source-handle system (e.g., Smith2019_p18_C3) so reviewers can follow your trail in under a minute.

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