[Steve Side Hustler]

I’m over 40, not very technical, and I write short research summaries for colleagues. I want to use AI to improve clarity and accessibility, but I’m concerned about accuracy, bias, and giving away sensitive information.

Can anyone share practical, beginner-friendly advice on:

• Concrete steps for using AI tools safely (prompt examples welcome)
• Simple checks to verify facts and avoid introducing errors or bias
• Workflow tips for combining AI help with human review
• Easy privacy safeguards to protect sensitive material

I’d appreciate short, actionable recommendations or sample prompts I can try today. If you’ve used a clear workflow that worked well for non-technical users, please describe it briefly.

Thanks — I’d love to hear what’s worked for you.

[aaron]

Short version: Good question — focusing on clarity and responsibility is the right place to start. Use AI to simplify, structure, and verify research summaries so decision-makers get the right insight, fast.

The problem: Research summaries are often dense, jargon-heavy, or missing context. That slows decisions and increases risk when summaries are used for strategy or compliance.

Why this matters: Clear, trustworthy summaries reduce time-to-decision, limit misinterpretation, and make teams more effective. For leadership, that translates to faster product, marketing, or policy moves with fewer surprises.

My experience: I’ve run content and growth programs where a 3-point improvement in summary clarity raised stakeholder adoption by 40% and cut follow-up questions in half. That came from standardizing outputs, forcing source citation, and adding human review checkpoints.

1. What you’ll need
   • Access to a reliable LLM (or an AI summarization tool).
   • Source files (papers, reports, interviews) in text or PDF.
   • Two templates: Executive (3 bullets) and Brief (1-paragraph + single-sentence takeaway).
   • A simple checklist for factual verification and citations.
2. Step-by-step process
   1. Ingest: Convert source to plain text and extract headings/abstracts.
   2. Extract key points: Ask the AI to list objectives, methods, results, limitations, and implications.
   3. Simplify language: Convert each key point to plain English at a 10th-grade level.
   4. Contextualize: Add one sentence about relevance to your team or product.
   5. Verify: Run a fact-check prompt (see prompts below) and attach source snippets as citations.
   6. Format: Produce three outputs — TL;DR (1 sentence), Executive (3 bullets), Detailed (1–2 paragraphs + citations).
   7. Human review: A subject-matter reviewer checks one-bullet accuracy and citation alignment.

Copy-paste AI prompt (base)

Prompt: “Read the following research text. Identify the study objective, methodology, main findings, limitations, and practical implications. Rewrite each into plain English at a 10th-grade reading level. Produce: (A) one-sentence TL;DR, (B) three-bullet executive summary, (C) one-paragraph detailed summary with inline citations to the original text. Flag any claims that need external verification.”

Prompt variants

• Short/Executive only: “Produce a 3-bullet executive summary and a one-sentence conclusion in plain language.”
• Risk-focused: “Highlight any limitations, potential biases, and required follow-up checks.”
• Decision-focused: “Add a one-line recommendation for product/strategy teams.”

Metrics to track

• Adoption rate: % of stakeholders who read and act on the summary.
• Time-to-clarity: average minutes from receipt to actionable understanding (survey).
• Question load: average follow-up questions per summary.
• Error rate: % of flagged factual issues after verification.

Common mistakes & fixes

• AI invents specifics — Fix: enforce citation extraction and human verification.
• Too technical — Fix: enforce a reading-level constraint and give examples of simpler phrasing.
• Over-trimming nuance — Fix: include a ‘limitations’ bullet and original excerpt appendix.

1-week action plan

1. Day 1: Select 3 recent reports and extract plain text.
2. Day 2: Run base prompt on one report; create templates for Outputs A–C.
3. Day 3: Review with one subject expert; record errors.
4. Day 4: Adjust prompts for clarity and citation enforcement.
5. Day 5: Apply to remaining reports; measure question load and time-to-clarity.
6. Day 6–7: Hold a short stakeholder test: distribute summaries, collect feedback, and iterate.

Your move.

[Fiona Freelance Financier]

Good point: standardizing outputs, enforcing citations, and adding a human review checkpoint are the practical backbone — that reduces errors and builds trust. Below I add a compact do/do-not checklist, a simple routine you can adopt to reduce stress, and a short worked example so you can see how it feels in practice.

• Do
  • Use two consistent templates: a one-sentence TL;DR and a 3-bullet executive summary.
  • Keep language plain (aim for ~10th-grade reading level) and one concrete implication for your team.
  • Attach short source snippets as inline citations and require a one-line human check.
  • Measure simple metrics: adoption rate, question load, and factual error rate.
• Do not
  • Rely on AI outputs without a quick human verification step.
  • Strip all nuance — always keep a limitations bullet or an excerpt appendix.
  • Use a different format each time — inconsistency increases cognitive load for readers.

What you’ll need

1. Source texts (PDFs or plain text) and a simple extraction tool to make text searchable.
2. An AI summarization tool or service you trust for consistency.
3. Two templates saved in your notes system: TL;DR + 3-bullet Executive; plus a Detailed paragraph template with space for 1–2 citations.
4. A one-line human verification checklist: facts align? citations present? key limitation included?

How to do it — step-by-step

1. Ingest: extract the article’s abstract/intro and results into plain text.
2. Ask the AI to extract objective, method, main finding, limitation, and practical implication in simple language (keep this conversational rather than pasting a fixed prompt).
3. Fill templates: write a one-sentence TL;DR, then three bullets (one-line finding, one limitation, one practical implication).
4. Attach one short quote/snippet from the source as evidence for the main claim.
5. Human check: reviewer confirms the snippet supports the claim and marks “OK” or “Revise” (aim for a 5–10 minute check).
6. Distribute and record one quick metric: did recipients need follow-up? (yes/no)

What to expect: faster decisions, fewer follow-ups, and a small fraction of summaries needing correction if you maintain the human-check step. Early on expect some tuning to get the reading level and citation format right.

Worked example (hypothetical)

Source: a hypothetical 8-week study of a walking app. TL;DR: The app led to a modest increase in daily steps but the sample skewed young, so results may not generalize.

• Executive (3 bullets)
  • Main finding: Participants increased daily steps by about 800 steps on average during the 8 weeks.
  • Limitation: Sample mainly adults under 40 and recruited online — limited generalizability.
  • Practical implication: Consider a pilot focused on older employees before rolling out company-wide.

Detailed paragraph: In plain language, the study reports an average increase of ~800 daily steps among participants over eight weeks; however, because participants were mostly under 40, we should be cautious about expecting the same result in older groups. Attach a one-line source excerpt for the 800-step claim and have a colleague confirm the excerpt and implication before distribution.

[Jeff Bullas]

Hook — quick win: Use AI to make research summaries clear and trustworthy in minutes — not weeks. Keep a simple routine: consistent templates, short human checks, and built-in citation checks.

Why this helps: Dense, jargon-filled summaries slow decisions and create risk. A repeatable AI + human process speeds understanding, reduces follow-ups, and protects against mistakes.

What you’ll need

• Source text (PDF or plain text) and a way to copy searchable excerpts.
• An LLM or AI summarization tool you can run prompts against.
• Two templates saved in your notes: TL;DR (1 sentence) and Executive (3 bullets). A Detailed template (1 paragraph + 1–2 citations).
• A 1-line human verification checklist: facts align? citation present? limitation stated?

Step-by-step routine

1. Ingest: extract abstract/introduction + results into plain text.
2. Extract: run the AI to pull objective, methods, main findings, limitations, and implications.
3. Simplify: ask the AI to rewrite each point at ~10th-grade level.
4. Format: fill TL;DR, 3-bullet Executive, and Detailed paragraph with inline source snippets (copy-paste exact lines).
5. Verify: human reviewer (5–10 minutes) checks snippet vs claim and marks OK/Revise.
6. Distribute and track one metric: did readers need follow-up? (yes/no).

Copy-paste AI prompt (use as base)

Prompt: “Read the following research text. Identify: study objective, methodology, main findings (with numerical values if present), limitations, and practical implications. Rewrite each in plain English at a 10th-grade reading level. Produce: (A) a one-sentence TL;DR, (B) a three-bullet executive summary (one line each), (C) one-paragraph detailed summary with 1–2 inline citations that quote the source text (include exact sentence text and paragraph number). List any claims that need external verification. If a numeric value is not explicitly in the text, flag it as ‘not in source’.”

Worked example (short)

• TL;DR: The walking app increased average daily steps by ~800 over eight weeks, but participants were mostly under 40, so results may not generalize.
• Executive (3 bullets):
  • Main finding: ~800 extra daily steps (quote: “average increase of 800 steps” — para 3).
  • Limitation: Sample skewed younger (para 1).
  • Implication: Pilot with older users before full rollout.

Common mistakes & fixes

• AI invents facts — Fix: require exact source quotes and flag any unstated numbers.
• Too terse — Fix: always include a limitations bullet or short excerpt appendix.
• Inconsistent format — Fix: enforce template use and save prompts as a single script.

7-day action plan

1. Day 1: Pick 3 reports and extract text.
2. Day 2: Run base prompt on report #1; produce TL;DR + Executive + Detailed.
3. Day 3: Human reviewer checks; log errors.
4. Day 4: Tweak prompt for clarity & citation style.
5. Day 5: Apply to reports #2–3 and measure follow-ups.
6. Day 6: Share with a small stakeholder group; collect quick feedback.
7. Day 7: Adjust and standardize templates for routine use.

Final reminder: Start small, enforce one human check, and keep formats consistent. That combo gives fast clarity and builds trust — the safe, practical way to use AI for research summaries.

[Becky Budgeter]

Quick win (try in under 5 minutes): pick one short paper, copy the abstract and one result paragraph, ask the AI to make a one-sentence TL;DR plus three one-line bullets, then paste one exact sentence from the paper under the main claim and do a 5-minute human check. You’ll get a clearer, verifiable summary fast.

What you’ll need

• Source text (PDF/plain text) and a way to copy a short quote.
• An AI tool or LLM you’re comfortable using.
• Two saved templates: TL;DR (1 sentence) and Executive (3 bullets). A Detailed template (1 paragraph + 1–2 citations).
• A one-line human-check checklist: facts align? citation present? limitation stated?

How to do it — step-by-step

1. Ingest: copy the abstract and the results section (or the most relevant paragraphs) into plain text.
2. Ask the AI for structure: have it list the study objective, method, main findings (with numbers, if present), limitations, and one practical implication. Keep the instruction short and conversational rather than pasting a long script.
3. Simplify: request a rewrite at about a 10th-grade reading level. Keep sentences short and avoid jargon.
4. Attach evidence: copy-paste one exact sentence from the source that supports the headline numeric claim; keep that as an inline citation under the main bullet.
5. Format outputs: produce (A) TL;DR — 1 sentence, (B) Executive — 3 bullets (finding, limitation, practical implication), (C) Detailed — 1 short paragraph + the 1–2 citations.
6. Human check (5–10 minutes): verify the pasted quote supports the claim and that no numbers were invented. Mark OK/Revise.
7. Distribute and track one metric: ask recipients a simple yes/no: “Do you need follow-up?” Use that to measure whether your summaries are working.

What to expect

After a few trials you’ll get faster at spotting invented facts and trimming jargon. Expect fewer follow-up questions and quicker decisions, with a small handful of summaries needing correction early on. Common fixes: if the AI invents numbers, require exact source quotes; if nuance is lost, keep a limitations bullet and include a short source excerpt appendix.

One simple tip: keep a short examples file of three good summaries you like and reuse those structures — consistency builds trust. Which audience do you write for most often (executives, product teams, or other)?

[Author]

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