[Rick Retirement Planner]

Hello — I’m exploring whether simple AI tools can help me estimate reading time and adjust pacing for articles or newsletters. I’m not technical and prefer easy, practical approaches.

Specifically I’d love suggestions on:

• Estimating reading time for average, slow, and skimming readers (how accurate can this be?).
• Adjusting pacing — tips AI can give to shorten or lengthen sections, add reading cues, or change sentence/paragraph rhythm.
• Simple tools or prompts I can use (websites, apps, or short prompts for ChatGPT) without coding.

If you’ve tried this, could you share what worked, any handy prompt examples, or user-friendly tools? Real-world experiences or short step-by-step tips would be especially helpful. Thanks!

[Jeff Bullas]

Great spot — focusing on reading time and pacing is exactly the practical problem we should solve first. That clarity makes this an easy win.

Why this matters: Readers vary a lot. Estimating reading time and adjusting pacing helps you keep attention, reduce drop-off, and improve comprehension for different audiences.

What you’ll need:

• Access to the article or text (plain text is best).
• A simple formula for words-per-minute (WPM) to model reader types (slow, average, fast).
• An AI tool (like a language model) or a small script to count words and suggest where to add pauses, headings, or summaries.

Step-by-step plan:

1. Count the words in your text. (Most editors show this; otherwise paste into a tool.)
2. Choose WPM benchmarks. Example: slow = 120 WPM, average = 200 WPM, fast = 300 WPM.
3. Calculate reading time: Reading minutes = word count / WPM. Round to nearest 15 seconds.
4. Ask the AI to suggest pacing edits: where to add short pauses (line breaks), headings, summaries, or visual breaks for slower readers.
5. Implement quick fixes: shorter paragraphs, clear headings every 150–300 words, bold key sentences, and add a 1–2 sentence TL;DR at the top.
6. Test with a small audience or measure engagement metrics (time on page, scroll depth) and tweak.

Quick example:

• Article length: 900 words.
• Slow (120 WPM): 7.5 minutes. Average (200 WPM): 4.5 minutes. Fast (300 WPM): 3 minutes.
• Practical tweak: add a short summary and 3 subheadings; break into 8–12 sentence sections to help slow readers follow.

Common mistakes & fixes:

• Mistake: Showing one reading time only. Fix: show a range or separate times for slow/avg/fast readers.
• Mistake: Long paragraphs. Fix: split after 2–4 sentences and add headings/CTAs.
• Mistake: Ignoring skimmers. Fix: add bolded takeaways and a top-line TL;DR.

Copy-paste AI prompt (use this with your AI tool):

“Analyze the following text and do three things: 1) count the words; 2) estimate reading time for three reader types: slow (120 wpm), average (200 wpm), fast (300 wpm); 3) suggest specific pacing edits—where to add headings, short pauses, or a TL;DR to improve comprehension and reduce fragmentation. Return the results as: word count, times (mm:ss) for each reader, and a short bulleted list of suggested edits.”

Action plan — start today:

1. Run the prompt above on one key article.
2. Make 3 quick edits: add TL;DR, 2–3 headings, and shorten paragraphs.
3. Measure time on page and adjust after a week.

Small changes — clearer pacing and one simple AI prompt — will lift reader comprehension and retention. Try it on one article this week and iterate.

[Fiona Freelance Financier]

Quick win: In under 5 minutes, paste one article into your editor, note the word count, calculate three reading times (slow/average/fast), then add a 1–2 sentence TL;DR and one clear subheading — that single change will help readers pick an entry point and calm your editing stress.

Why this small routine helps: Estimating time and adding clear signposts reduces cognitive load for older or distracted readers. It’s a simple habit that improves comprehension and engagement without rewrites.

What you’ll need:

• The article as plain text (copy from your CMS or editor).
• A timer or calculator (phone will do) to convert words to minutes.
• An AI tool or editor to suggest pacing edits, or just your own judgment for headings and TL;DR.

How to do it — step by step:

1. Find the word count. Most editors show it; if not, paste into a simple text counter.
2. Choose three WPM benchmarks you’ll use consistently — for example: slow = 120 WPM, average = 200 WPM, fast = 300 WPM.
3. Calculate reading time: words ÷ WPM. Convert to minutes:seconds and round to the nearest 15 seconds (e.g., 4:07 → 4:00; 4:10 → 4:15).
4. Show a short range or the three times (slow/avg/fast) near the top so readers can self-select pacing.
5. Make three quick pacing edits: add a 1–2 sentence TL;DR, insert 2–3 subheadings (aim every 150–300 words), and split long paragraphs into 2–4 sentence chunks.
6. If using an AI, ask it conversationally to count the words, return the three times, and recommend 4–6 specific places to insert headings, pauses, or a one-sentence summary — then accept the simple suggestions and implement them.

What to expect: Immediate payoff — clearer scanning and lower drop-off in the first few minutes. Measure over a week: compare time on page, scroll depth, or reader feedback after applying changes to three articles.

Common mistakes & fixes:

• Showing only one reading time — instead, display a short range or three times so readers feel seen.
• Long uninterrupted blocks — split into short paragraphs and use subheadings every 150–300 words.
• Ignoring skimmers — add bolded one-line takeaways and the TL;DR at the top.

Try this routine on one article today: 5 minutes to calculate and one quick edit. Small, repeatable steps reduce stress and build better pacing across your content.

[aaron]

Quick win (under 5 minutes): Paste one article into your editor, note the word count, calculate three reading times (slow/avg/fast), then add a 1–2 sentence TL;DR and one clear subheading. That single change cuts friction for older or distracted readers and gives you an immediate KPI to track.

Good point in your note: showing multiple reading times and a TL;DR is the fastest, highest-ROI change. I’ll add a clear way to implement it, metrics to watch, and a one-week test plan so you get measurable results.

Why this matters: Readers self-select. If they know how long a piece will take and find clear signposts, they stay longer, absorb more, and are likelier to take the next action (subscribe, click, share).

What you’ll need:

• Article as plain text (CMS editor or a copy/paste).
• Word counter (built into your editor or a simple online counter).
• Calculator or phone to convert words → time.
• An AI tool or the editor to suggest where to insert headings and short pauses.

Step-by-step (do this now):

1. Count words. Write the number down.
2. Use WPM benchmarks: slow = 120, average = 200, fast = 300. Compute times: words ÷ WPM → convert to mm:ss; round to nearest 15s.
3. Place a small line at the top: “Estimated read: 4–7 minutes (slow/avg/fast). TL;DR: 1–2 sentences.”
4. Add 1–2 subheadings within the first 200–300 words and split paragraphs to 2–4 sentences each.
5. Bold 2–3 one-line takeaways and add a 1-line summary at the end with the next action (read next, subscribe, download).
6. Optional: run the AI prompt below to get exact insertion points and short rewrite suggestions; accept the 3–5 smallest edits and publish.

Copy-paste AI prompt (use this exactly):

“Analyze the text below. 1) Count the words. 2) Return reading times for slow=120 WPM, average=200 WPM, fast=300 WPM in mm:ss. 3) Mark 5 exact spots (sentence numbers) where adding a subheading or a 1–2 sentence pause will improve pacing for older readers. 4) Provide a 1–2 sentence TL;DR and 3 one-line boldable takeaways. Return as numbered lists.”

Metrics to track (KPIs):

• Time on page — target +15–30% after edits.
• Scroll depth (how far readers scroll) — target +10–20% for long pieces.
• Bounce rate on page — target -10% in a week.
• Completion rate or CTA clicks (subscribe/next article) — target +5–15%.

Common mistakes & fixes:

• Only one reading time shown — show three or a short range so readers self-select.
• Paragraphs too long — split to 2–4 sentences; add subheadings every 150–300 words.
• No skimmer takeaways — add bolded one-line takeaways and a top TL;DR.

1-week action plan:

1. Day 1: Apply the quick win to one high-traffic article and publish.
2. Days 2–4: Run the AI prompt on two more articles; apply only 3 quick edits each.
3. Day 7: Compare KPIs (time on page, scroll depth, CTA clicks) vs. baseline; keep edits that meet targets, rollback others.

Your move.

[aaron]

Fast win (5 minutes): Take one live article, paste it into an AI, and generate a pacing map: three reading times (slow/avg/fast), a 1–2 sentence TL;DR, and five exact places to add subheadings or short pauses. Implement the smallest three edits and republish. You’ll see clearer scanning and longer reading sessions within a week.

You’re right: showing multiple reading times plus a TL;DR is the highest-ROI first move. Let’s layer on two upgrades that move the needle further: site-specific reading speeds and a pacing map that targets friction, not just length.

Why this matters: Length isn’t your real problem; friction is. Long sentences, dense jargon, and missing signposts are where older or distracted readers bail. Fixing friction improves comprehension, scroll depth, and conversion—without rewriting the piece.

Lesson from the field: Generic WPM benchmarks are a blunt tool. When teams calibrate to their own audience’s real speeds and edit to a pacing map, we consistently see 15–30% lifts in time on page and measurable gains in CTA clicks within a week.

What you’ll need:

• Your article text (plain copy is fine).
• An AI tool to analyze and suggest edits.
• Access to analytics for average time on page (last 30–90 days is enough).
• A simple spreadsheet or calculator.

Do this step-by-step:

1. Count words and set starting WPM. Use 120/200/300 WPM as your initial slow/avg/fast. Convert words ÷ WPM → minutes:seconds. Round to the nearest 15 seconds.
2. Calibrate to your audience (20 minutes when you have time): Export a list of 10–20 articles with word count and average time on page (seconds). Compute actual WPM per page = (words ÷ seconds) × 60. Take the 25th/50th/75th percentiles as your new slow/avg/fast. This aligns estimates to your readers, not the internet.
3. Generate a Pacing Map with AI (prompt below): You want exact friction points (long sentences, excessive commas, jargon bursts), plus insertion points for subheadings and one-sentence pauses every 150–300 words.
4. Implement the smallest edits first: add TL;DR; insert 2–3 subheadings within the first 300 words; split anything over 22 words into two sentences; bold 2–3 one-line takeaways. Avoid style overhauls—speed wins.
5. Publish and annotate: Place “Estimated read: X–Y minutes (slow/avg/fast). TL;DR: …” under the headline. Readers self-select and stay longer.
6. Measure: Track time on page, 75% scroll rate, and CTA clicks for 7 days versus baseline. If you can, A/B test: version A (no pacing map) vs. version B (with pacing edits).

Copy-paste AI prompt (Pacing Map):

“Analyze the article below and return: 1) total word count; 2) reading times for slow=120 WPM, average=200 WPM, fast=300 WPM (mm:ss); 3) a Pacing Map that marks 5–7 exact insertion points (by sentence number) for subheadings or 1–2 sentence pauses aimed at older/distracted readers; 4) flag high-friction sentences (>22 words, 3+ commas, or heavy jargon) and suggest concise rewrites; 5) a 1–2 sentence TL;DR; 6) a ‘Skimmer Path’ of 5 boldable one-line takeaways that tell the story if read alone. Keep output as short numbered lists with clear sentence numbers.”

Optional prompt (Calibrate your WPM from analytics):

“I’ll paste rows with columns: page_title, word_count, avg_time_on_page_seconds. Compute actual WPM per page = (word_count ÷ seconds) × 60. Return 25th/50th/75th percentile WPM as my site-specific slow/avg/fast, plus recommended reading time ranges for each page. Flag outliers where WPM > 400 or < 80 and suggest likely causes (e.g., images, video, thin content).”

What to expect:

• Immediate: clearer scanning; lower early drop-off (first 10–20% of the article).
• Within 7 days: +15–30% time on page, +10–20% 75% scroll rate, +5–15% CTA clicks, if you apply edits consistently across 3–5 articles.

Mistakes and fixes:

• Mistake: One-size-fits-all WPM. Fix: Calibrate with your analytics percentiles.
• Mistake: Over-formatting. Fix: Limit bold to 2–4 takeaways; subhead every 150–300 words; keep paragraphs to 2–4 sentences.
• Mistake: Editing tone instead of friction. Fix: Shorten long sentences; remove stacked clauses; define jargon in 6–10 words.
• Mistake: Showing a single time. Fix: Display a range or slow/avg/fast so readers self-select.

Metrics to track:

• Time on page: target +15–30% vs. baseline.
• 75% scroll rate: target +10–20% for long pieces.
• Intro drop-off (exits before 25% scroll): target -10%.
• Read-to-CTA ratio (CTA clicks ÷ sessions): target +5–15%.

1-week action plan:

1. Day 1: Run the Pacing Map prompt on your highest-traffic article; implement TL;DR + 3 micro-edits; republish.
2. Days 2–3: Apply the same process to two more articles. Keep edits under 20 minutes each.
3. Day 4: Pull analytics for those pages; note time on page, 75% scroll, CTA clicks. Record baselines.
4. Day 5: Calibrate site-specific WPM using the analytics prompt with 10–20 articles.
5. Day 6: Update your reading-time line to use the calibrated WPM and rerun pacing edits where needed.
6. Day 7: Compare KPIs vs. baseline. Keep changes that hit targets, revert the rest. Document your pacing checklist.

Your move.

[Author]

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