You’re Using ChatGPT Wrong (According to 700M Users)
Notes #13 - Why asking > doing, and how to turn prompts into business decisions.
Hey everyone! 👋🏼
Josep here, back with your weekly bite of career insights and encouragement ✨
A quick gut-check:
When you picture ChatGPT, what’s the first image that pops up?
Someone cranking out SQL? Debugging Python? Auto-drafting emails?
That was my picture too, until I dug into a new OpenAI study covering hundreds of millions of users and billions of messages.
The data flipped my mental model.
🧭 In today’s issue:
What 700M+ people actually do with ChatGPT (fast facts you can use)
3 implications for your data career (beyond “code faster”)
Copy-paste prompt formulas + a context checklist to make your outputs land
Stick with me for ~5 minutes; this one can upgrade how you work starting today. ✨
Let’s dive in! 👇🏻
The surprising reality
From May 2024 → June 2025, usage exploded.
Now there are more than 2.5B messages/day (!!!).
But here’s the twist:
3 buckets dominate (~80%):
Practical Guidance, Seeking Information, Writing.Coding is small. ~4.2% of all messages (Data Analysis ~0.4%).
At work, writing wins. ~40% of work messages are writing/editing/clarifying text. Two-thirds of that is editing your text, not generating from scratch.
Intent matters: ~49% Asking, 40% Doing, 11% Expressing.
“Asking” is growing faster and is rated higher quality than “Doing.”
Most usage isn’t work. Non-work messages grew from 53% to ~73%.
Image from How People Use ChatGPT. Table 1 reports ChatGPT’s daily message volume (in millions), split into work vs. non-work use, shown as 7-day averages from sampled conversations ending June 26, 2024 and June 26, 2025. Who’s using it? Nearly half of adult messages come from <26 years old;
usage is surging in lower-income countries; the early gender gap has largely closed.
Translation: People aren’t just “getting AI to do tasks.”
They’re using it to think, decide, and write more clearly.
Why this matters for you (data folks)
It’s tempting to think of AI purely as a “doer”: something that can code faster, write boilerplate reports, or automate workflows.
But the reality is that people use it most for thinking and communication support.
And that should make us pause.
Because the biggest opportunities for us as data professionals aren’t just in making ChatGPT write SQL faster. They’re in using it as a partner to sharpen how we think, ask questions, and explain context.
Leverage = writing + context
Dashboards rarely fail because of SQL. They fail because they don’t say what it means for a decision. LLMs excel at(a) sharpening your thinking
(b) editing your words so stakeholders “get it.”
“Soft” use ≠ soft impact
The next generation of workers arrives expecting AI at every step, scoping, brainstorming, drafting, revising, and explaining.If you only use AI for code, you’re leaving most of the value on the table.
Edge beats execution
If only ~4% of usage is coding, “I code with AI” won’t differentiate you.Owning the problem framing, asking better questions, and adding context will.
How you can use this insight today
Use these as is. They’ll lift the quality of outputs immediately.
1) Decision Framing (for analysis, memos, or dashboards)
You are my writing co-pilot.
- Context: [business model], [audience], [decision & timeframe], [constraints], [data caveats].
- Goal: Draft a concise brief that answers:
1) What changed?
2) Why?
3) So what?
4) Now what? (next actions + owner + when)
- Tone: Clear, non-jargon.
Max 250 words. Add a 1-line TL;DR.2) Stakeholder Translation (tech → business)
You are a translator for non-technical execs.
- Input: [technical finding/SQL/Python output].
- Audience: [role, e.g., VP Sales]. Decision needed: [X by Y].
- Rewrite:
1) 3 bullets: impact on revenue/cost/risk
2) Confidence & caveats
3) A single recommended action with owner & deadline3) Editing Your Draft (LLM’s #1 work use)
You are my editor.
- Audience: [e.g., CX leadership].
- Objective: [approve plan].
Revise this draft for clarity, brevity, and flow.
Replace jargon, keep numbers, keep nuance.
Return:
1) improved text
2) 3 notes on what you changed and why4) Analysis Planning (prevent dead dashboards)
You are a senior analytics lead.
- Problem: [business question].
- Metric(s): [define].
- Decisions: [which levers may change].
Design an analysis plan: hypotheses, required data, cuts/segments, pitfalls, success criteria, and the “decision table” we’ll hand to stakeholders.5) “Asking” beats “Doing” (quality boost)
Before doing anything, ask me 5 clarifying questions that would change the output, then propose 2 alternative approaches and their trade-offs.
Goal: best possible answer for [audience/decision].The Context Checklist (paste next to your IDE)
Before you ask an LLM to write, code, or summarize, check these:
Audience: who is this for (role, context, reading level)?
Decision: what choice will this enable, and by when?
Metric(s): which KPI matters, what’s “good vs bad”?
Constraints: data limitations, guardrails, must-haves, off-limits.
Caveats: sampling bias, freshness issues, “unknowns.”
Format: bullets vs memo, 1-pager vs slide, TL;DR required?
If you can’t answer these, your output will look polished but won’t move a decision.
Common traps to avoid
Prompting with verbs, not context. (“Write SQL for churn” → meh.)
Pretty output, no decision. If the reader can’t act, you didn’t finish.
Forgetting the audience. Exec ≠ PM ≠ engineer. Rewrite per role.
Over-trusting summaries. Always add caveats + confidence.
TL;DR you can share
Most people use ChatGPT to think and write, not just to code.
For data pros, the win is context → better decisions → impact.
Lead with Asking (clarify), then Doing (draft), finish with Editing (for audience).
Your turn
How do you mostly use ChatGPT right now — Asking, Doing, or Editing?
Hit reply with one example and the audience you wrote for. I’ll feature a few (anonymously) in next week’s Note.
Stay curious,
— Josep
P.S. If this helped, forward it to a teammate who’s still using LLMs only for code. They’re missing 80% of the upside.
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