Don't ask for the answer, ask for the script

Ask an AI assistant the same thing twice and you get two different answers. Sometimes only the wording changes. Sometimes the number changes. That’s not a defect: it’s the normal behavior of a system that, as we saw in chapter 2, rolls the dice on the most plausible continuation.

For writing an email, great — variety is welcome. For adding up this month’s reimbursement spreadsheet, it’s a problem.

Is all work the same kind of work?

Probabilistic: each run can come out different. This is generative AI’s natural mode. It shines where there’s no single right answer: drafting, summarizing, interpreting a confusing request, proposing alternatives, reading a messy document.

Deterministic: same input, same output, every time. This is code’s natural mode. It shines where there’s only one right answer: adding, filtering, converting, moving a file, applying a tax rule.

The classic mistake is using the first to do the second’s job. I’ve fallen for it myself: I spent days trying to get an agent to reorganize a complicated spreadsheet before I realized the case was deterministic and asked for the code — I told the story in When I brought a bazooka to kill a fly. I recorded a pretty rough video about this a while back — deterministic vs. probabilistic agentic processes — and the idea has only become more important since.

There’s a bit of math that helps you remember why. If you chain three probabilistic steps that get it right 70%, 90%, and 98% of the time, the chain as a whole gets it right close to 61% of the time. Every step that becomes deterministic code drops out of that multiplication.

What if the AI wrote the code instead of the answer?

This is the big shift in using AI “as code” instead of “as chat.” Instead of asking:

Add up the amount column in this spreadsheet.

I ask:

Write a script that adds up the amount column in this spreadsheet, run it, and show me the result.

In the first case, the model estimates a total. In the second, it uses the part it’s really good at — writing a short program — and the one doing the math is the computer, which doesn’t get sums wrong.

The difference in practice:

Where do I use this?

• Removing the background from an image. I could ask an image model to “redraw it without the background” and hope it doesn’t touch the rest. I’d rather ask for a script that cuts out the background: whatever isn’t background stays untouched, pixel by pixel.
• Data analysis. When I ask for the median time things sit idle at each stage of a process, I don’t want the model to “guess” the median. I want the Python code that calculates it, the charts that code generates, and the file saved in the project so I can run it again next month.
• Routines. Tasks that repeat become scripts kept in a runbooks folder in the repository. Next time, the agent doesn’t reinvent anything: it runs what already exists.

This is the opposite of needing to know how to program. I don’t write these scripts — I describe what I need, read what was done, and check the result. What changed is that expertise now arrives in a format I can read: the agent explains, in plain language, what the script does and why. Knowing this option exists changes what you ask for. I talked about getting over the fear of it in Product people: don’t be afraid of a script.

How do you decide?

Three quick questions before asking for anything:

1. Is there a single right answer? If so, ask for code.
2. Will I do this again? If so, ask for code and keep it.
3. Will someone hold me accountable for the result later? If so, ask for code — you’ll want to show how you got there.

If all three answers are “no,” go ahead and just chat. And many good tasks mix the two: the AI interprets the messy request (probabilistic) and calls a script to execute it (deterministic). A good agent is, at heart, that combination.