Jeff Bullas

Yes — quickly and reliably. AI can create teaching rubrics aligned with Bloom’s Taxonomy that you can use, adapt and test in a single class period.

Why it works: Bloom gives clear action verbs and levels. AI translates those verbs into observable criteria and performance descriptors. You supply the learning goal and context; the AI supplies the structure and wording.

What you’ll need

• One clear learning objective (student-facing sentence).
• Grade/age level and subject.
• Number of rubric levels (3–5 is ideal).
• 2–4 assessment criteria (e.g., clarity, evidence, reasoning, technique).
• An AI chat tool (ChatGPT, Bard, etc.) or any LLM interface.

Step-by-step

1. Write a single, precise learning objective. Example: “Students will write a persuasive essay that argues a position using evidence and counterarguments.”
2. Pick 3–4 criteria you’ll assess (e.g., thesis, use of evidence, organization, grammar).
3. Use the copy-paste prompt below. Paste it into your AI and run it. Ask for a 4-level rubric aligned to Bloom’s verbs.
4. Review and tweak language to match your class. Shorten descriptors or add examples if needed.
5. Test with one student sample or a quick self-assessment checklist. Adjust scores/descriptors once after real use.

Core copy-paste prompt (use as-is)

“Create a 4-level rubric aligned to Bloom’s Taxonomy for this objective: [Insert objective]. Grade level: [Insert grade]. Subject: [Insert subject]. Assess these criteria: [list criteria]. For each criterion, provide performance descriptors for levels: Excellent (Create/Evaluate), Proficient (Analyze/Application), Developing (Understand), Beginning (Remember). Keep descriptors short, observable, and student-friendly. Output as a clear rubric with each criterion and four levels.”

Prompt variants

• Short version for quick formative checks: “Give me a 3-level quick-check rubric for [objective] with criteria: [criteria]. Use Bloom verbs and short student-friendly descriptors.”
• Project rubric for group work: “Create a rubric for a group project on [topic] that includes collaboration and individual accountability, aligned to Bloom’s Taxonomy.”

Example (brief)

• Criterion: Use of Evidence
  • Excellent (Create/Evaluate): Integrates multiple credible sources and critically evaluates counterarguments.
  • Proficient (Analyze/Application): Uses relevant sources and explains how they support the argument.
  • Developing (Understand): Includes some sources but limited connection to the claim.
  • Beginning (Remember): Minimal or no use of supporting evidence.

Mistakes and fixes

• Vague descriptors —> Make them observable: replace “good understanding” with “explains 3 supporting points with examples.”
• Too many levels —> Use 3–4 to keep decisions consistent.
• Not aligned to Bloom —> Map each level to a Bloom verb (Remember, Understand, Apply, Analyze, Evaluate, Create).

Action plan (next 20 minutes)

1. Write one clear objective.
2. Pick 3 criteria.
3. Copy the core prompt, paste into your AI, generate the rubric.
4. Quickly review and simplify language for students.
5. Use with one class and note one tweak to make next time.

Quick reminder: Start small, iterate fast. AI gives a fast, draftable rubric — your judgment makes it classroom-ready.

Quick win: Paste 6–8 messy bullets into an AI and ask for a 300-word blog with a headline, intro, three subheads and a call-to-action. You’ll have a draft in under 5 minutes.

Nice question — the idea of turning messy notes into publishable posts is exactly the high-leverage use of AI. Below is a simple, repeatable workflow you can follow right away.

What you’ll need

• Your messy notes (typed or transcribed). Even shorthand works.
• An AI writing tool (Chat-style models like ChatGPT or similar).
• A short style guide: voice (friendly, professional), target audience (e.g., small business owners), desired length.
• 5–30 minutes, depending on polish level.

Step-by-step: from notes to polished post

1. Collect and reduce: Put all notes in one place and remove obvious duplicates. Keep the key facts, quotes, links.
2. Ask for an outline: Paste notes and request a 3–5 point outline. Expect a structure you can refine in 1–2 minutes.
3. Generate a draft: Use the outline and a clear prompt (example below) to create a 300–700 word draft.
4. Edit for voice & accuracy: Read, tweak facts, add examples or personal anecdotes. Keep sentences short and active.
5. Polish headline & CTA: Ask AI for 5 headline options and pick one. Add a one-line CTA (subscribe, download, comment).
6. Final pass: Check readability, add subheads, bullets, and one image idea. Done.

Copy-paste AI prompt (use as-is)

Here’s a robust prompt you can paste into the AI:

“I have these notes: [paste your notes]. Please turn them into a clear, friendly blog post for small business owners. Format: headline, 2-3 sentence intro, three subhead sections of 2–3 short paragraphs each, a one-sentence actionable takeaway, and a call-to-action to sign up for a weekly newsletter. Tone: practical, encouraging, simple language. Length: about 500 words. Highlight one example and one quick step the reader can try immediately.”

Example (before → after)

• Before (notes): “email list, lead magnet free PDF, webinar, more sales.”
• After (AI): headline: “How a Simple Free PDF Can Multiply Your Email List” + intro + 3 subheads explaining why a lead magnet works, how to create one fast, and how to use it in a webinar funnel, finishing with an immediate step: create a 1-page PDF outline today.

Common mistakes & fixes

• Too vague prompts → be specific about audience, tone, length.
• Blind trust in AI facts → always verify numbers, names, claims.
• Over-editing to perfection → ship a helpful draft, then iterate based on feedback.

Action plan (next 15 minutes)

1. Gather one page of notes.
2. Use the copy-paste prompt above and generate a draft.
3. Pick a headline and publish the draft as a short post or newsletter.

Try that quick win now—turn one messy page into a live post. Small habit, big payback.

Spot on: adding a simple “trigger” column and validating one persona fast keeps this practical and testable. Let’s turn that into a repeatable system you can run monthly without guesswork.

The upgrade: add disqualifiers and lost-deal notes so AI separates “who buys” from “who browses.” That’s the lever that saves time and ad spend.

• Do: include columns for status (won/lost), primary objection, and switch_from (what they used before).
• Do: ask AI to output one negative persona (who to ignore) alongside your top persona.
• Do: mine verbatim phrases from emails — those become headlines that actually get clicks.
• Don’t: build personas without a clear buying trigger and budget band.
• Don’t: overfit to demographics; anchor on pains, triggers, and success metrics.

What you’ll need

• 10–50 rows in a spreadsheet with columns: company, role, industry, revenue/purchase_size, pain (verbatim), purchase_reason, trigger, status (won/lost), objections, switch_from, time_to_value_days, channel_source.
• An AI chat tool (GPT-style).
• One quick validation channel (a $50 ad, 30 targeted messages, or a small email batch).

Step-by-step (40–75 minutes)

1. Export + tidy (10 min): Pull 10–50 recent interactions. Remove names. Keep short verbatim pain lines.
2. Tag outcomes (5 min): Mark each row won/lost and add the main objection and switch_from if known.
3. Ask AI to cluster (10 min): Use the prompt below to get 2–4 clusters with ICPs, personas, and one negative persona.
4. Choose one persona (5 min): Pick the cluster with the clearest trigger and fastest time_to_value.
5. Message mining (5 min): Ask AI to extract the top 10 power phrases customers used (verbatim) about pains and outcomes.
6. Create one asset (10 min): Write a single ad or 2-sentence email using a power phrase plus a measurable outcome.
7. Validate (3–7 days): Launch to a narrow audience. Track CTR, reply rate, and cost per lead.
8. Decide (5 min): If results beat baseline, scale. If not, pivot to the next cluster.

Copy-paste AI prompt (premium ICP Canvas + personas)

“I will paste a CSV with columns: company, role, industry, revenue_or_purchase_size, pain_verbatim, purchase_reason, trigger, status_won_lost, primary_objection, switch_from, time_to_value_days, channel_source. Do the following:
1) Build an ICP Canvas: firmographics, economic buyer role, top 3 pains (with verbatim examples), primary triggers, expected budget band, success metrics (how they measure value), and 5 disqualifiers.
2) Cluster into 2–4 customer groups. For each, provide: name, concise ICP, top 3 pains, primary trigger, budget band, typical objections, and a 120-word persona with 3 messaging hooks and best outreach channels.
3) Create 1 Negative Persona: who looks interested but rarely buys, with reasons.
4) Output a 6-question validation checklist to test the leading persona via ads or outreach.
Return results in clear sections. Keep language plain and specific.”

Worked example (how this looks in practice)

• Business: Scheduling software for home services.
• ICP snapshot: 5–50 staff HVAC, plumbing, or electrical firms; owner-operator or ops manager; peak-season overload is the trigger; budget $150–$400/month.
• Persona: “Owner-Operator Owen” — runs a 12-person HVAC shop, misses calls during rush, wants fewer no-shows and faster scheduling. Measures success by booked jobs/day and technician utilization. Biggest objection: “Switching will be a hassle.” Trigger: summer heatwave backlog. Channels: Facebook local groups, Google Local Services.
• Negative persona: Solo handymen with inconsistent demand who churn at month 2.
• Message mined from verbatims: “Stop losing calls at lunch,” “No-shows kill my Saturdays,” “I need jobs slotted in under 2 minutes.”
• Test asset: Ad headline — “Cut no-shows 30% and book jobs in 2 minutes. No downtime.”
• Validation metric: CTR above your average by 30% or reply rate above 5% within 7 days.

Insider trick: ask AI to split clusters by time_to_value_days and switch_from. Fast time-to-value + a painful switch_from (e.g., spreadsheets) usually indicates a persona you can win fast with “switch without downtime” messaging.

Common mistakes and quick fixes

• Averages hide winners — Fix: look at metrics by persona cluster, not overall.
• Feature-speak — Fix: reuse the customer’s own phrases about outcomes in your headlines.
• Too much demographic detail — Fix: lead with pains, triggers, and success metrics.
• No disqualifiers — Fix: ask AI for 5 crisp disqualifiers and apply them in targeting.
• Overfitting early data — Fix: re-run the clustering monthly with 10–20 new rows.

What to expect from the AI output

• 1–2 high-confidence personas with clear triggers and budget bands you can target immediately.
• 1 negative persona to exclude from ads and outreach.
• A shortlist of verbatim phrases that become headlines and email openers.
• A simple 6-question checklist to validate with a $50 ad or 30 messages.

7-day action plan

1. Day 1: Export 10–50 rows; add status, objections, and switch_from; sanitize.
2. Day 2: Run the ICP Canvas prompt; select one persona + one negative persona.
3. Day 3: Mine 10 verbatim phrases; draft one ad and one 2-sentence email.
4. Day 4–6: Launch a small test; measure CTR/reply/CPL by persona.
5. Day 7: Keep the winner; kill or rework the rest. Document disqualifiers in your targeting.

Bonus prompt (message mining)

“From the pasted customer emails, extract the 10 most persuasive verbatim phrases customers used about pains and outcomes. Do not paraphrase. Rank by intensity and frequency. Then write 5 ad headlines and 3 two-sentence emails using those exact phrases.”

Start with real rows, separate buyers from browsers, and let the verbatims write your headlines. That’s how you find an ICP you can actually win — fast.

Quick win (under 5 minutes): Paste 10 customer emails or support notes into this prompt and ask the AI: “Give me the top 3 pain points and one-sentence Ideal Customer Profile.” You’ll get instant clarity to use in a single targeted ad or outreach.

Nice addition — Aaron’s sprint approach is perfect for momentum. I’d add a few small, high-leverage tweaks so that the persona you test is practical, testable, and tied to real buying behavior.

What you’ll need

• 10–25 real customer rows (company, role, pain, why they bought, ARR or purchase size)
• A spreadsheet (CSV)
• An AI chat tool (GPT-style)
• A small validation channel ready (email batch, $50 ad, or 30 LinkedIn messages)

Step-by-step — do this in 30–60 minutes

1. Export: Pull 10–25 recent rows into a sheet. Add a short “trigger” column if you can (event, deadline, regulation, cost-cutting).
2. Sanitize: Remove personal names. Keep short verbatim pain lines — they’re the hooks.
3. Cluster with AI (5–10 min): Use the prompt below to get 2–4 clusters and crisp persona drafts.
4. Create one test persona (5 min): Pick the clearest cluster. Ask AI for 3 messaging hooks and top 2 channels.
5. Draft outreach (10 min): Use AI to write a 2-sentence email or a single ad headline tailored to that hook.
6. Validate (3–7 days): Run the small test. Track reply rate, CTR, CPL.
7. Decide (5 min): If it beats your baseline, scale; if not, pick the next cluster and repeat.

Copy-paste AI prompt (use exactly):

“I will paste a CSV with columns: company, role, pain, purchase_reason, revenue, trigger. Please cluster these rows into 2–4 customer groups. For each group, give: 1) name, 2) concise ICP (company size, industry, role), 3) top 3 pain points (verbatim examples), 4) primary buying trigger, 5) expected budget band, 6) a 120-word persona with 3 messaging hooks and best outreach channels.”

Example output (short)

Persona: “Operations Olivia” — Mid-market e-commerce operations manager. Pain: inventory shortages, slow fulfillment, high returns. Buying trigger: peak-season stockouts. Budget: $5k–$20k. Messaging hooks: reduce stockouts by 40%, cut fulfillment time in half, improve returns accuracy.”

Common mistakes & fixes

• Relying on hypotheticals — Fix: always start with real rows, even messy ones.
• Too many vague fields — Fix: add a single trigger column (it surfaces why they bought).
• Overcomplicating validation — Fix: one ad or 30 messages is enough to disprove a persona quickly.

7-day action plan

1. Day 1: Export 10–25 rows and add a trigger column.
2. Day 2: Run the clustering prompt and pick 1 persona.
3. Day 3: Refine messaging and create one ad/email.
4. Day 4–7: Run validation, measure CTR, replies, CPL; decide whether to scale or iterate.

Start simple, test fast, and let the data tell you which persona to double down on.

Quick win: You can get to a clean, customer‑friendly Terms of Service or simple contract in under 90 minutes with AI — then use a lawyer to tighten the risk points. Here’s the playbook plus a ready‑to‑use clause library.

Do / Don’t checklist

• Do: Set a target length (1–2 pages) and reading level (about Grade 8). Ask the AI for headings and bullets.
• Do: Feed concrete facts (trial length, refund window, support hours). Vague in = vague out.
• Do: Keep a “3‑way consistency” check: ToS, privacy policy, and payment provider rules must match.
• Do: Bold the 3 most important user‑impact clauses (refunds, auto‑renewal, data use) for conspicuousness.
• Don’t: Copy legal boilerplate you don’t understand. Ask AI to translate every clause into plain English.
• Don’t: Skip jurisdiction. Tell the AI your governing law and where customers are.
• Don’t: Publish without a human legal review for your region and industry.

What you’ll need

• One‑sentence product summary and who it serves.
• Business model: free/paid, one‑time/subscription, trial length, refund window, auto‑renewal rules.
• Top 3 risk areas: refunds, IP/content, data/privacy.
• Jurisdiction: country/state, and any industry rules you know apply.
• Support process: how users cancel, contact support, and request refunds.

Step‑by‑step

1. Baseline draft: Use the prompt below to get a labeled, plain‑English ToS (1–2 pages).
2. Detail pass: Replace placeholders. Add exact numbers and steps (how to cancel, response times).
3. Tone & clarity: Ask AI to rewrite for Grade 8 reading and add a 5‑bullet “at‑a‑glance” summary.
4. Clause toggles: Choose from the clause library below (refunds, license, user content, liability cap). Toggle strict vs friendly.
5. Scenario test: Run the edge‑case prompt to simulate refunds, chargebacks, and termination. Fix gaps.
6. Consistency check: Align with your privacy policy and payment provider rules. Make dates, numbers, and terms match.
7. Conspicuousness: Bold key consumer terms (refunds, auto‑renewal, liability limits). Keep sentences short.
8. Legal review: Send the final draft with a one‑page summary of your risks and questions.

Insider trick: Clause toggles (copy/paste and tune)

• Refunds (friendly): “We offer a [X‑day] trial. After the trial, payments are non‑refundable except where the law requires. Billing errors? Contact us within [Y days]; we’ll investigate and fix confirmed errors.”
• Refunds (stricter): “All charges are final after the [X‑day] trial. We may grant discretionary credits for confirmed billing errors reported within [Y days].”
• License to use (digital content): “We grant you a personal, non‑transferable, non‑exclusive license to access and use the Service and content for your own, non‑commercial purposes while your account is active.”
• User content ownership: “You own the content you upload. You give us a license to host, display, and process it only to provide and improve the Service.”
• IP protection: “We own the Service and original content. You agree not to copy, resell, or reverse engineer the Service.”
• Liability cap: “To the extent allowed by law, our total liability for any claim is limited to the amount you paid to us in the last [12] months.”
• Termination: “You can cancel anytime in your account. We may suspend or end access for misuse or non‑payment. We’ll try to notify you unless the law or security prevents it.”
• Governing law: “These Terms are governed by the laws of [State/Country], without regard to conflict‑of‑laws rules.”

Worked example: Digital template store (subscriptions + downloads)

• At‑a‑glance: Personal license, auto‑renewing monthly plan, 14‑day refund for technical issues we can’t fix, support by email in 2 business days.
• Eligibility: “You must be 18+ and able to agree to contracts.”
• Account & billing: “Plans renew monthly until you cancel. You can cancel at any time in your account; this stops future charges.”
• Trials & refunds: “7‑day free trial. After that, charges are non‑refundable, except within 14 days for confirmed technical issues we can’t resolve after you contact support.”
• License: “Use templates for your own business. You can edit them. You cannot resell or share them as templates.”
• Content & IP: “We own the templates; you own your projects. You allow us to process your content to run and improve the Service.”
• Disclaimers & limits: “We provide templates ‘as is.’ To the extent allowed by law, our liability is limited to fees paid in the last 12 months.”
• Termination: “Misuse or non‑payment may lead to suspension. We’ll try to notify you first.”
• Governing law: “[Your State/Country].”

Robust AI prompts to copy‑paste

• Baseline ToS: “Draft a 1–2 page, Grade‑8 reading level Terms of Service for [ProductName], which [one‑sentence what it does and for whom]. Model: [free/one‑time/subscription], trial: [length], refund: [rules], auto‑renewal: [yes/no]. Jurisdiction: [Country/State]. Include clear sections: Eligibility; Account & Billing; Trial & Refunds; Content Ownership; License to Use; Acceptable Use; Privacy/Data Use (brief, consistent with [my privacy summary: …]); Disclaimers & Liability Cap; Termination; Governing Law. Output: labeled clauses, a 5‑bullet ‘Key Points’ summary, and a list of items a lawyer should review for [Jurisdiction].”
• Edge‑case test: “Given this ToS, simulate five scenarios: (1) user cancels on day 8 of trial, (2) card fails on renewal, (3) user requests refund for download after 20 days, (4) alleged IP infringement in user upload, (5) account banned for scraping. For each, state the outcome, user steps, and where the ToS needs clearer language.”
• Clarity rewrite: “Rewrite this ToS at Grade‑8 reading level, shorten sentences, convert long paragraphs into bullets, and bold the three clauses that most affect consumers. Keep meaning intact.”

Common mistakes & fixes

• Vague refunds → Specify exact windows, processes, and exceptions.
• Hidden auto‑renewal → Put it near pricing, use plain language, and bold it.
• Mismatch with privacy policy → Reuse identical phrases for data types and retention.
• No process steps → Add “how to cancel” and “how to request a refund” with response times.
• Over‑legalese → Run the clarity rewrite prompt; aim for short sentences and bullets.

1‑week action plan

1. Day 1: Write your one‑liner, list the top 3 risks, choose refund and license toggles.
2. Day 2: Run the baseline ToS prompt; fill placeholders and numbers.
3. Day 3: Run clarity rewrite; add the 5‑bullet summary; bold key consumer terms.
4. Day 4: Scenario test; fix gaps; align with privacy and payment provider.
5. Day 5: Internal read‑through (support/sales) to spot friction points.
6. Day 6: Send to lawyer with your risk list and questions.
7. Day 7: Implement feedback; publish; track support tickets and conversion changes.

Closing thought: AI gets you from blank page to credible draft fast. Your edge is clarity, consistency, and conspicuousness. Keep it short, human, and aligned with how customers actually use your product — then let your lawyer tune the sharp edges.

Quick hook

If you want real-time CAC:LTV that actually helps you act — not just pretty charts — keep it simple, prove the flow on one channel, then scale. AI speeds detection and reduces manual digging.

Why AI helps here

• AI can stitch noisy event streams and surface anomalies faster than manual checks.
• It automates cohort LTV projections and recommends threshold adjustments as data matures.

What you’ll need

1. Data sources: ad spend by campaign, acquisition events (who, when, source), revenue events (orders/subscriptions).
2. Storage/processing: a place to join events in near real-time (e.g., BigQuery, Snowflake, or a lightweight event layer).
3. Rules: definitions for CAC and LTV (rolling 7/30/90 or cohort lifetime).
4. Visualization & alerts: dashboard + automated alerting (email/Slack/webhook).

Step-by-step to a working real-time flow

1. Pick one paid channel and one cohort window (start with 30 days).
2. Stream data: push ad spend per campaign and acquisition events into your processing layer as they occur.
3. Join and aggregate: for each rolling 30-day window, compute total_spend and new_customers per campaign.
4. Compute cohort revenue: sum revenue for those new_customers inside 30 days of acquisition and divide by cohort size to get LTV_30d.
5. Calculate CAC = total_spend / new_customers and CAC:LTV = CAC / LTV_30d. Smooth with a 7-day moving average for alerts.
6. Set alerts: e.g., trigger if CAC:LTV changes by more than 20% vs the 7-day moving average or if CAC exceeds X dollars per customer.

Practical example

If spend = $50,000, new_customers = 500 and 30-day revenue = $150,000: CAC = $100, LTV_30d = $300, CAC:LTV = 1:3. If the 7-day moving ratio drops to 1:1.5, an alert flags campaign or attribution issues.

Common mistakes & fixes

• Mistake: trusting raw attribution. Fix: validate with last-touch + channel-weighted checks.
• Mistake: alert fatigue. Fix: use moving averages and progressive thresholds (soft then hard alerts).
• Mistake: measuring overall average only. Fix: monitor cohorts and channels separately.

Immediate action plan (next 7 days)

1. Choose one channel (e.g., Google/Facebook) and 30-day cohort.
2. Stream spend + acquisition + revenue to one place and run the calculations above.
3. Deploy a simple alert: 20% change vs 7-day average to Slack/email. Iterate thresholds after two weeks.

Copy-paste AI prompt (use with your LLM or analyst)

“Act as a data analyst. I have three tables: ad_spend(campaign_id, date, spend), acquisitions(user_id, campaign_id, acquired_at), revenue_events(user_id, event_date, amount). For a rolling 30-day window, produce SQL queries that calculate per-campaign: total_spend, new_customers, CAC=total_spend/new_customers, cohort_30d_revenue=sum(amount for revenue events within 30 days of acquired_at), LTV_30d=cohort_30d_revenue/new_customers, CAC_LTV_ratio=CAC/LTV_30d. Then provide anomaly rules to alert when CAC_LTV_ratio changes >20% vs 7-day moving average, and list three likely causes and first diagnostic queries to run.”

One quick question

Which tools are you using now for ads, customer tracking, and payments (examples: Google Ads, Facebook Ads, GA4, Segment, Stripe, BigQuery)? Tell me and I’ll give exact next-step SQL/automation examples you can copy-paste.

You’re close. Here’s the tighter, faster way to turn Twitter/X and Reddit into a simple “trend radar” you can run in under an hour a week — with clearer signals and fewer false positives.

High-value tweak: Don’t just count mentions. Score each signal by three things: velocity (is it growing week-over-week?), intensity (how strong is the sentiment?), and intent (are people asking how-to or purchase questions?). That score tells you what to test first.

What you’ll need

• Twitter/X and Reddit accounts.
• One sheet (Google Sheets or CSV) with columns: Date, Source, Text, Link, Author type, Keyword matched, New terms, Sentiment (+/−/0), Is question (Y/N), Engagement (likes/replies/upvotes).
• Basic automation (Zapier/Make or a simple script). Manual copy-paste is fine to start — aim for 200–500 posts/week.
• Any LLM that accepts pasted text or a CSV.

Setup (once) — lean and reliable

1. Seed lists (10–15 terms): split into Core (niche, product, pain phrases) and Adjacent (tools, formats, competitor terms). Example pain phrases: “anyone else”, “how do I”, “worth it”, “stuck with”.
2. Capture rules: save post text, timestamp, link, and engagement. Exclude posts with identical text or obvious reposts. If manual, grab the top 30–50 posts per keyword per week.
3. Weekly batch: collect for 3–5 days, then run AI to enrich (cluster, sentiment, questions, new terms). Keep a rolling baseline from last week.

Insider trick: the Trend Score (0–15)

• Velocity (0–5): compare mentions in the last 72 hours vs the prior 7-day average. 0 = flat/declining, 3 = +25–50%, 5 = +100% or more.
• Intensity (0–5): strength of sentiment and engagement. 0 = mixed/low, 3 = clear tilt + decent engagement, 5 = strong tilt + high engagement.
• Intent (0–5): proportion of posts that are questions or solution-seeking. 0 = few questions, 3 = some buying/how-to language, 5 = many “how do I/what’s best/anyone using” posts.

Prioritize anything scoring 10+ and showing up on both Twitter and at least two subreddits within 48–72 hours.

Copy-paste prompts (refined and ready)

• 1) Enrich and clean your raw posts

Act as my social trend analyst. You’ll receive posts from Twitter/X and Reddit about [NICHE]. Tasks: (1) remove duplicates or near-duplicates (>85% similar), (2) label each post with sentiment (positive/negative/neutral), (3) mark IsQuestion = Yes/No, (4) extract up to 3 new terms not in my seed list [SEED LIST], (5) tag a topic cluster label in plain English. Return a numbered list of concise post summaries with: Source, Date, Cluster, Sentiment, IsQuestion, NewTerms, EngagementHint (high/med/low).

• 2) Build the weekly trend brief with scores

Here are ~300 cleaned social posts about [NICHE] from the last 7 days. Using a 7-day baseline I describe as [BASELINE NOTES], produce: (1) top 5 emerging themes with 1–2 example snippets each, (2) top 10 rising keywords/hashtags with relative change vs baseline, (3) sentiment split and notable shifts, (4) top 6 recurring questions, (5) a Trend Score (0–15) for each theme based on Velocity, Intensity, Intent, and (6) one tactical test per theme with expected 7-day KPIs. Keep it concise, numbered.

• 3) Decide the next action (fast validation)

Given these themes and scores: [PASTE THEMES], recommend the single highest upside test to run this week. Specify audience, channel (Twitter or Reddit), message angle, format (poll/thread/post), and a minimal success metric (e.g., 3%+ reply rate or 200+ poll votes). Include one variant to A/B test.

What to expect

• First week: noisy. That’s normal. You’re calibrating keywords and filters.
• By week 2–3: repeatable weekly brief, 1–2 high-confidence tests, clearer hit rate.
• Wins look like: faster engagement on “fresh” topics, cheaper ad tests, and clearer language for landing pages.

Worked example (new niche): menopause fitness

• Seeds: “menopause workouts”, “perimenopause strength”, “hot flashes exercise”, “sleep recovery”, “HRT + training”.
• Signals (250 posts): rising mentions of “zone 2 cardio for sleep”, questions about “protein timing at 40+”, backlash to long fasted training.
• Top theme: Short strength + zone 2 combo sessions (Trend Score 12: velocity +110%, intent-heavy questions, positive sentiment).
• Action: Post a 3-move 20-min routine thread and a poll: “What’s harder right now? Sleep, recovery, or consistency?” Success = 3%+ replies or 300+ poll votes in 72 hours.

Upgrade your sheet with two quick formulas (manual-friendly)

• Novelty count: each week, tally New terms that appear at least 3 times. New + repeated = strong early trend.
• Question density: % of posts with IsQuestion = Yes for a theme. Over 30% usually means solution-seeking — great content fodder.

Common mistakes & fixes

• Mistake: treating total volume as truth. Fix: prioritize velocity vs last week and cross-source confirmation.
• Mistake: ignoring repost storms. Fix: dedupe near-identical posts; favor unique authors.
• Mistake: vague actions. Fix: force one test per theme with a simple KPI and a 7-day window.
• Mistake: keyword drift. Fix: review your seed list monthly; retire low-signal terms, add new adjacent terms.

7-day plan (with thresholds)

1. Day 1: Finalize Core and Adjacent seed lists. Set up capture to your sheet (text, timestamp, link, engagement).
2. Days 2–3: Collect 200+ posts. Run the Enrich prompt. Adjust filters to drop obvious noise.
3. Day 4: Run the Weekly brief prompt. Score themes. Shortlist any 10+ Trend Score with cross-source confirmation.
4. Day 5: Use the Decision prompt to pick one test. Write copy and one A/B variant.
5. Day 6: Launch the test. Track replies, poll votes, CTR, or saves.
6. Day 7: Review. If KPI met, scale with a longer thread, newsletter section, or small ad. If missed, tweak the angle or retire the theme.

Bottom line: Let AI compress the noise, but you set the bar: velocity + intensity + intent. Score it, test fast, and you’ll spot — and act on — real trends before they’re obvious.

Onward — you’ve got this.

You’re close — now make that pilot irresistibly useful and sellable in a week. Use a simple rhythm that turns book wisdom into action: Teach → Show → Do → Check. It keeps lessons short, practical, and bingeable.

Why this works

• People buy outcomes, not lectures. This rhythm moves learners from idea to action in minutes.
• It also speeds production: you’ll reuse book content, keep videos short, and ship faster.

What to have ready

• One chapter or theme that solves a painful problem in 60–90 minutes.
• A 200–300 word sample of your writing (for AI to learn your tone).
• Any diagrams, stories, or checklists from that chapter.
• Quiet room, phone or webcam, and a simple editor.

Build the module with the Teach → Show → Do → Check template

1. Define the outcome and proof. Write one outcome and how a learner proves it in 10 minutes (a checklist, a draft plan, a before/after metric).
2. Storyboard each lesson with LOFA: Lead (hook in 20s), Outcome (what they’ll do), Framework (your 3–5 steps), Action (1-minute task). Three short lessons usually cover a module.
3. Build the workbook first. Create 1–2 fill-in worksheets that mirror your steps. This becomes the “Do” and the evidence of progress.
4. Make slides fast. 6–10 slides per lesson, one idea per slide, big fonts, no paragraphs. Put stories and examples in your voice-over.
5. Record in sprints. Hit record, teach one slide per minute, pause, continue. Two takes max. Trim the start/end and add captions.
6. Check learning. Add a 5–6 question quiz or a short checklist. Tie every item to the outcome.
7. Price and position. Pilot price can be 3–5x your book price for a single module if it solves a pressing problem. Add two clear bonuses (worksheet pack and a 20-minute group Q&A).

Insider prompts you can copy-paste

• Outcome + rubric: “Here is a chapter excerpt and my learner profile: [paste]. Extract 1 measurable module outcome and a simple pass/fail rubric a beginner can complete in 10 minutes. Suggest the minimal evidence a learner should submit.”
• Voice lock: “Learn my tone from this 250-word excerpt: [paste]. Summarize my voice in 5 traits. Then rewrite this lesson intro [paste] in that voice, 120 words, warm and direct.”
• Workbook generator: “Based on this lesson script [paste], create a 1-page worksheet with 5 fill-in fields, 1 reflection question, and a checklist of 6 steps. Keep text concise and learner-facing.”
• Slides in bullets: “Turn this lesson outline [paste] into 8 slides. Give me: Slide title + 3 bullets max + one simple visual idea (no images, just description). Keep it clean and high-contrast.”
• Scenario quiz: “Create 6 multiple-choice questions from this lesson [paste]. Make 3 recall and 3 scenario-based decisions. Provide answers with a one-sentence explanation.”
• Pre-sale blurb: “Write a 120-word invite for a paid pilot of this module [paste outline], focusing on the outcome, who it’s for, what they’ll finish, and the pilot price. Add 3 bullet ‘You’ll walk away with…’ benefits.”

Example: turning a chapter into a tight module

• Chapter: “Beat Procrastination at Work.”
• Module outcome: “By the end, you’ll ship one meaningful task before noon for 5 days using a simple system.”
• Lessons (5–10 minutes each):
  • Lesson 1 — Teach: The 10:30 Rule (plan, 50-minute focus, 10-minute log).
  • Lesson 2 — Show: Watch me plan a morning using the template.
  • Lesson 3 — Do + Check: You plan tomorrow; upload your 10-minute log; quick quiz.
• Activity: Print the “Morning Sprint” worksheet and complete it once during the lesson; schedule 5 repeats.
• Quiz sample: “Which part of the 10:30 Rule protects deep work?” (Answer: schedule the 50-minute block before 10:30).

Pricing and upsell in plain numbers

• Pilot module: 3–5x your book price if it delivers a fast win (example: $49–$149).
• Flagship (all modules): 8–12x book price, especially if it includes worksheets, templates, and a Q&A.
• Upsell ideas: a 60-minute group workshop; a done-with-you review of a learner’s worksheet.

Production timebox (realistic, low-tech)

1. 60 minutes: outcome + LOFA storyboard + worksheet draft.
2. 60 minutes: slide bullets + first pass script.
3. 90 minutes: record all 3 lessons and quick edits.
4. 30 minutes: build quiz, upload files, set price, write pilot blurb.

Common mistakes and quick fixes

• Lecture sprawl: If a lesson runs over 12 minutes, split it. One idea per lesson.
• No proof of progress: Add a 10-minute deliverable (worksheet, checklist, draft) tied to the outcome.
• Flat delivery: Start with a 20-second promise and a 10-second story or example.
• Accessibility gaps: Add captions and provide the slides as a PDF.
• Scope creep: Pilot one module. Park everything else in a “later” list.

2-day launch plan (do-first)

1. Morning Day 1: Run the “Outcome + rubric” and “Slides in bullets” prompts. Finalize storyboard and worksheet.
2. Afternoon Day 1: Record lessons in two takes, trim starts/ends, export.
3. Morning Day 2: Build quiz, upload assets, set a pilot price, write the pre-sale blurb with the prompt above.
4. Afternoon Day 2: Invite 10–20 readers. First goal: 10 paid enrollments or 10% of invites by Friday.

Keep it tight, keep it practical, and ship the pilot. One polished module proves the model — then you scale with confidence.