[Rick Retirement Planner]

Hello — I’m interested in using large language models (LLMs) to turn technical research (papers, whitepapers, or lab reports) into clear, practical business language for executives and teams.

My main goals are: create concise executive summaries, highlight market or product implications, and suggest simple next steps. I’m not technical, so I’d like a gentle, reliable approach I can follow.

Can anyone share a practical workflow, example prompts, or templates? Specifically, I’d love tips on:

• Step-by-step workflow (how to prepare input, prompt, and edit output)
• Sample prompts for executive summaries, slide bullets, and plain-English explanations
• How to check accuracy and avoid hallucinations (fact-checks, citation tips)
• Simple, user-friendly tools or interfaces for non-technical users

Thanks — any examples or quick templates you can paste here would be very helpful!

[Steve Side Hustler]

Good question — focusing on turning technical research into clear business language is exactly the right starting point. That intention (translate for decisions, not reproduce the paper) makes the rest much easier.

• Do: Ask for short, action-oriented outputs (1-paragraph executive summary, 3 business implications, 1 recommended next step).
• Do: Give the model the audience and the metric you care about (cost, time-to-market, customer impact).
• Do: Break a long paper into 1–2 page chunks and work iteratively — saves time and avoids hallucination.
• Do not: Expect perfect technical accuracy without a quick human sanity check of key facts.
• Do not: Ask for everything at once; dense inputs produce fuzzy outputs.

Practical micro-workflow you can use in 20–40 minutes (for busy people):

1. What you’ll need: the research PDF or abstract, a 1-line audience definition (e.g., CFO, product lead), and one metric that matters (e.g., unit cost, time-to-market, or market size).
2. Quick skim (5–10 min): Open the paper, highlight the title, abstract, conclusion and any figures/tables showing results. Copy 2–4 sentences that state the key claim and 1 table/figure caption.
3. Chunk & translate (5–10 min): Feed the model one chunk at a time and ask for a plain-English sentence for each chunk. Ask it to avoid jargon and to use the named audience and metric. Keep each request short and focused.
4. Turn into business points (5–10 min): Ask the model to convert those plain-English sentences into: 3 business implications, 1 risk/uncertainty, and 1 concrete next step. Specify the output format: short bullets and one-sentence rationale each.
5. Quick fact-check (5 min): Verify any numeric claims (percentages, costs) against the paper’s tables; correct the summary if numbers don’t match.

Worked example (short): you have a 12-page battery chemistry paper and need a one-paragraph brief for the product manager. Read abstract+conclusion, copy the two key result sentences and the main figure caption. Ask for plain-English sentences that link results to cost or lifetime. Then ask for three short business implications (e.g., lower production cost, faster charge time, supply-chain sensitivity), one risk (scale-up unknowns), and a single recommended next step (small pilot and supplier check). Expect a one-paragraph exec summary plus 3 bullet implications; total output should be scannable in 60–90 seconds.

Small habit that helps: save a template with your audience + metric + desired output structure and reuse it. Over time you’ll spot which papers are decision-ready and which need more lab validation — and you’ll get faster at turning research into business moves.

[aaron]

Quick win: Copy the paper’s abstract and conclusion, paste them into this prompt (below), and you’ll get a one-paragraph executive summary + 3 business implications in under 5 minutes.

The problem: Technical papers are written for peers — full of jargon, caveats and experimental details. Decision-makers need clear implications, risks and a recommended next step, not a translation of methods.

Why this matters: Faster, reliable translation of research into business language reduces time-to-decision, avoids wasted pilots and focuses budget on the experiments that move KPIs.

What I learned: The single biggest levers are (1) tell the model who the audience is and which metric matters, and (2) feed it small chunks. That combination keeps answers actionable and reduces hallucinations.

1. What you’ll need: PDF or abstract, a 1-line audience (e.g., CFO), and one metric that matters (unit cost, time-to-market, NPS).
2. How to do it — step-by-step:
   1. Quick skim (5 min): copy the title, abstract, conclusion and any result captions (2–4 sentences + 1 figure caption).
   2. Chunk (5–10 min): feed the LLM one chunk at a time. Ask for a single plain-English sentence per chunk, aimed at your named audience and metric.
   3. Business conversion (5–10 min): ask the LLM for: 1-paragraph exec summary, 3 business implications (1 line each + one-sentence rationale), 1 risk, 1 concrete next step with owner/time estimate.
   4. Fact-check (5 min): verify any numbers against the paper’s tables; correct the model if they don’t match.
3. What to expect: A scannable brief you can use in a meeting (60–90 seconds to read). Expect to need one quick human sanity check for numeric accuracy.

Copy-paste AI prompt (use this exactly — replace placeholders):

You are an executive summarizer. Read the following text: {PASTE ABSTRACT + CONCLUSION + MAIN FIGURE CAPTION}. Audience: Product Manager. Primary metric: unit cost. Output: (1) One-paragraph executive summary (<=70 words) in plain English, (2) Three business implications — one line each with estimated direction of impact (increase/decrease) on unit cost, (3) One key uncertainty, (4) One concrete next step with owner and time estimate. Avoid technical jargon.

Metrics to track (start with these):

• Time to decision per paper — target: reduce from 40 min to ≤15 min.
• % of papers flagged decision-ready (pilot or stop) — target: 20–30% initially.
• Numeric accuracy hit rate (paper vs summary) — target: ≥95% after fact-check.
• Pilot ROI / estimated cost-savings from implemented insights.

Common mistakes & fixes:

• Mistake: Asking for everything at once — output is fuzzy. Fix: Chunk the paper and iterate.
• Mistake: No audience or metric. Fix: Always start prompts with audience + metric.
• Mistake: Blind trust in numbers. Fix: Quick table check before presenting.

1-week action plan (practical):

1. Day 1: Pick one recent paper. Run the quick-win prompt and produce a 1-paragraph brief.
2. Day 3: Fact-check numbers and create 3 business implications.
3. Day 5: Run a 15-min internal review with the relevant stakeholder (product/CFO) and decide: pilot / archive / reject.
4. Day 7: Log outcomes and update your template (audience + metric + output format).

Your move.

[Jeff Bullas]

Quick win (3–5 minutes): Paste the paper’s abstract and conclusion into this prompt and ask for a one-paragraph executive summary + 3 business implications. You’ll have something meeting-ready in under 5 minutes.

Good point in your note — telling the model the audience and the metric, and feeding small chunks, is the single biggest lever. Here’s a concise, practical workflow that builds on that and gets you from research to decision fast.

What you’ll need

• PDF or abstract + conclusion (copy text).
• A one-line audience (e.g., Product Manager, CFO).
• The primary metric that matters (unit cost, time-to-market, customer retention).
• 5–20 minutes of focused time and a quick fact-check step.

Step-by-step (do this)

1. Skim 5 min: Copy the title, abstract, conclusion and 1 result caption/figure. Keep it short (2–6 sentences).
2. Chunk 5–10 min: If the paper is long, split into 1–2 page chunks. For each chunk, ask the model for one plain-English sentence aimed at your audience and metric.
3. Convert to business output 5–10 min: Ask for: (a) one-paragraph executive summary (<=70 words), (b) 3 business implications (one line each + direction on metric), (c) one key uncertainty, (d) one next step with owner and ETA.
4. Fact-check 5 min: Verify any numbers against the paper tables and correct the summary if needed.

Copy-paste AI prompt (use this exactly, replace placeholders):

You are an executive summarizer. Read the following text: {PASTE TITLE + ABSTRACT + CONCLUSION + MAIN FIGURE CAPTION}. Audience: Product Manager. Primary metric: unit cost. Output: (1) One-paragraph executive summary (<=70 words) in plain English, (2) Three business implications — one line each with estimated direction of impact (increase/decrease) on unit cost, (3) One key uncertainty, (4) One concrete next step with owner and time estimate. Avoid technical jargon.

Worked example (quick):

• Paper: battery chemistry improvement. You paste abstract+conclusion and run the prompt.
• Output you expect: 1-paragraph summary linking result to battery lifetime, 3 implications (lower cycle cost — decrease unit cost; faster charge — improves time-to-market; supply sensitivity — increases supply risk), one uncertainty (scale-up yield), and next step (run 100-unit pilot with manufacturing lead, 8 weeks).

Common mistakes & fixes

• Mistake: Asking for the whole paper at once. Fix: Chunk it and iterate.
• Mistake: No audience or metric. Fix: Always include them in the first line.
• Mistake: Blind trust in numbers. Fix: Quick table check before sharing.

3-step action plan (this week)

1. Day 1: Run the quick prompt on one recent paper and get the 1-paragraph brief.
2. Day 3: Fact-check numbers and convert to 3 business implications.
3. Day 5: Present the brief in a 10-min sync and decide: pilot / archive / reject.

Try the quick prompt now — small steps win. If you want, paste a short abstract here and I’ll show the exact output you should expect.

[aaron]

Right call on audience + metric + small chunks — that’s the lever. Here’s how to upgrade it from “nice summary” to a decision-ready brief with KPIs, evidence grades and a pilot plan.

The gap: Summaries still miss three things leaders need: the delta vs your baseline, the strength of evidence, and a low-risk next step with success criteria.

Why it matters: This moves you from interesting to investable. Faster yes/no decisions, fewer false starts, and clearer ROI.

Lesson from the field: Add three layers on top of your current workflow — baseline comparison, evidence grading, and a pilot design. That’s the difference between “we learned” and “we acted.”

What you’ll need: (1) Title + abstract + conclusion + one results figure/caption, (2) your current baseline for the key metric (e.g., unit cost $12.40, defect rate 1.8%), (3) stakeholder role (CFO/PM), (4) a budget/time guardrail for a pilot (e.g., ≤$25k, ≤8 weeks).

Do this step-by-step

1. Lock the facts (quote-only extraction). You want claims, limits and numbers exactly as written — no inventions.

Copy-paste prompt (Extraction):

Read the text between START and END. Extract: (1) the main claim, (2) all numeric results, (3) stated limitations/assumptions, (4) where results may not generalize. Quote every number verbatim from the text and list the exact sentence it came from. If a number is not present, write “No data”. START: {PASTE TITLE + ABSTRACT + CONCLUSION + FIGURE CAPTION} END.

1. Translate claims to KPI deltas vs your baseline. Force the model to compare to your status quo.

Copy-paste prompt (KPI delta):

Baseline metric: {e.g., Unit cost = $12.40; Yield = 92%}. Using ONLY the quoted numbers and claims you extracted, estimate the direction and plausible range of change vs baseline for {PRIMARY METRIC}. Output 3 bullets: (a) Direction (increase/decrease/uncertain), (b) Range (best/worst/most likely) with units, (c) Driver (1 sentence). If the paper doesn’t provide enough data, say “Uncertain” and name the missing input.

1. Grade the evidence. Leaders care how solid it is.

Copy-paste prompt (Evidence grade):

Using the extracted text, assign an evidence grade for the main claim: Exploratory (n<3, lab-only), Lab-replicated (n≥3), Pilot-scale (operational setting), Field-scale (multi-site). Justify in one line. Then list the top 2 threats to validity and how they’d bias the KPI (over/under).

1. Produce a decision brief. Keep it scannable and tied to KPIs.

Copy-paste prompt (Decision brief):

Audience: {CFO/Product Lead}. Primary metric: {e.g., unit cost}. Using the KPI delta and evidence grade above, produce: (1) 60–80 word executive summary in plain English, (2) 3 business implications with direction and estimated magnitude on {metric}, (3) 1 key uncertainty with a test to resolve it, (4) Go/No-Go recommendation with confidence (Low/Med/High) and rationale in one line.

1. Design a small, cheap pilot. Set success/fail up front.

Copy-paste prompt (Pilot design):

Constraints: budget ≤ {e.g., $25k}, time ≤ {e.g., 8 weeks}. Propose one pilot: scope, owner, sample size, data to collect, success threshold (numeric), fail-fast trigger (numeric), risks/mitigations, rough cost. Output 7 bullets, one line each.

Insider tricks

• Quote-locking: Always include “Quote every number verbatim” in extraction. It crushes hallucinations.
• Baseline-first: Feed your baseline before asking for impacts. Otherwise you’ll get generic upsides.
• Counter-brief: Ask for a one-paragraph skeptic view (“Why this won’t move the KPI”). Present both in exec reviews.

What to expect: A two-page brief you can read in 2 minutes: executive summary, KPI deltas with ranges, evidence grade, top risk, and a pilot with numeric gates. You’ll still do a 5-minute table check on numbers. Most briefs will be decisionable on the spot or after one clarification.

Metrics to track

• Time to decision-ready brief per paper — target: ≤15 minutes.
• Numeric accuracy after check — target: ≥98%.
• % briefs accepted by stakeholder without rewrite — target: ≥70%.
• Pilot hit rate (meets success threshold) — target: 30–50% early, improving over time.
• Cycle time from paper to pilot start — target: ≤2 weeks.

Common mistakes & fixes

• Vague impact (no baseline). Fix: Force delta vs current metric every time.
• Invented numbers. Fix: Quote-lock extraction + 5-minute table check.
• Over-weighting lab wins. Fix: Evidence grading + skeptic counter-brief.
• Bloated pilots. Fix: Budget/time guardrails and numeric stop/go gates.

1-week action plan

1. Day 1: Pick one paper. Run Extraction and KPI delta prompts. Log your baseline.
2. Day 2: Run Evidence grade + Decision brief. Do the 5-minute number check.
3. Day 3: Produce the Pilot design. Add budget/time and gates.
4. Day 4: 15-minute review with stakeholder. Decide: Pilot / Park / Reject.
5. Day 5: If Pilot, schedule kickoff; if Park/Reject, capture reason and assumptions.
6. Day 6: Create a reusable template with your baseline fields and the four prompts.
7. Day 7: Run the process on a second paper; compare metrics.

Your move.

[Author]

Posts