I used to think the formula bar was the hardest part of any spreadsheet. You stare at a wall of nested functions, and one wrong parenthesis blows up the whole model. So when I saw this post from an AI professional breaking down how to make sheets board-ready with plain-English prompts, I stopped scrolling immediately.
The original poster makes one point that stuck with me right away: don’t blindly run obvious spreadsheet prompts. The skill isn’t typing formulas anymore. It’s knowing exactly what to ask. And the creator backs it up with 30 prompts that quietly do an analyst’s work.
I broke down their whole approach below, plus the full prompt list so you can copy what fits your work.
Why this matters right now
The expert points out something I keep noticing too. These prompts are showing up everywhere. Board decks. Investor models. Monthly reviews. Even messy bank exports you’d normally dread cleaning up by hand.
They sound almost too simple. But that’s the trick. A short, clear instruction can replace an hour of manual grunt work, as long as you know which instruction to give. That’s the gap this post fills.
The 30 prompts, grouped by job
Here’s the full list the creator shared, organized by what you’re trying to accomplish. Copy the ones that match your task.
Setup and foundation:
- “Build me a [TYPE] spreadsheet from this data.”
- “List your top 10 assumptions before executing.”
- “Use named ranges so formulas read like English.”
- “Keep all inputs on an Assumptions tab.”
- “Format for board projection: clean palette, frozen rows.”
Building the structure:
- “Add a Dashboard tab with KPI tiles.”
- “Add Base / Bull / Bear scenario toggle.”
- “Add a P&L tab linked to the Revenue tab.”
- “Build a 12-month forecast by service line.”
- “Add a Funnel tab: audience to leads to deals.”
Analysis without formulas:
- “What trends stand out in 2026 vs 2025?”
- “Compare actuals to budget, explain 3 variances.”
- “Categorize these transactions into expense types.”
- “Which line items grow faster than revenue?”
- “Find the deals that closed fastest, what’s in common?”
Editing in Google Sheets:
- “Visualize @Revenue as a stacked bar.”
- “Summarize @Funnel in 5 bullets.”
- “Add conditional formatting on margin %.”
- “Translate @Dashboard into French.”
- “In @Assumptions, push Bull more optimistic, stay realistic.”
Debugging and cleanup:
- “Explain what the formula in [CELL] does in English.”
- “Trace [CELL] back to its source inputs.”
- “Why is [CELL] showing #REF / #VALUE / #DIV/0?”
- “Find any hardcoded numbers inside formulas.”
- “Show me how [CELL] connects to the Assumptions tab.”
Advanced moves:
- “Add a data table for revenue at diff growth rates.”
- “Add a Monte Carlo simulation on key drivers.”
- “Convert this monthly model to weekly granularity.”
- “Stress-test the model, what breaks first?”
- “Reconcile @Revenue tab with @P&L tab, find mismatches.”
The 5-step playbook for board-ready sheets
The list is great, but the real gold is the method the creator wraps around it. Here’s how they actually build a sheet you can defend in front of a board.
- Force the AI to expose its logic. Before anything else, ask it to list its top 10 assumptions. The original poster calls this non-negotiable, and I get why. If you don’t see the assumptions, you’re trusting a black box with your numbers.
- Separate inputs from logic. Keep every assumption on its own tab. Then pull any hardcoded numbers out of the formulas and into the open. When inputs live in one place, you can change a single cell instead of hunting through twenty formulas.
- Make it readable. Not “=B4*C7*1.12” that nobody can decode. Named ranges that read like plain English instead. The expert puts it bluntly: if a board member can’t follow it, it’s not board-ready.
- Audit before you send. Ask it to trace a cell back to its source inputs, then reconcile the Revenue tab against the P&L tab. This catches the mismatches that would otherwise surface live in the meeting, which is the worst possible time.
- Stress-test it. Ask “what breaks first?” You want to find the edge case in the model, not in front of investors. Better the spreadsheet cracks on your screen than on the projector.
The line that sums up the whole post for me: don’t trust a spreadsheet you can’t explain. If the AI built something you can’t walk through cell by cell, it isn’t finished, no matter how clean it looks.
My take
What I love about this approach is how it flips the workflow. The old bottleneck was technical skill, knowing the functions. The new bottleneck is thinking like an analyst, knowing what questions a model needs to survive scrutiny.
The prompts handle the mechanics. Your judgment handles the rest. That’s a much better trade, and it’s where this whole shift is heading across finance work.
This LinkedIn creator packed a serious amount of practical value into one post. Go read the full breakdown for the extra context behind each step, then try a couple of these prompts on your next model.