I’ve been reflecting on what it means to become a better prompt engineer. The field is expansive—prompt engineering for RAG applications? Better conversational interfaces? Building reliable agents? What does becoming a better prompt engineer even mean?

People talk about prompt engineering likes a bag of tricks—the emphasis is on the special phrasing, templates, intricate formatting. Staying current with new tips and techniques is important, but these shortcuts are a bit like all those fancy symbols on a scientific calculator (√, xʸ, log, Σ). Most higher-order mathematical operations are derived from four core operators (+, −, ×, ÷). When it comes to prompt engineering, I want to know what the basic operators are.

Say you spoke to a prompt engineer tackling a complicated, real-world problem in a production-grade piece of software. How much of their work do you think is going to be based on clever prompting tricks versus clearly understanding what they’re trying to achieve and systematically breaking it down into manageable steps for the LLM?

In my experience so far, the bottleneck always comes back to how well you understand what you’re trying to do. Can you articulate how you do something, despite all its nuance and complexity, using plain language?

The litmus test is whether you can teach me —someone reasonably intelligent but unfamiliar with your specific domain—how to reliably perform the task you want done, at the standard you expect. If you can teach me, I can teach the machine.

I’m not talking about fancy prompting techniques here. I’m talking about breaking a complicated task down into a series of clear steps that result in the output you want.

This is core operator stuff. That’s what I’m interested in.

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If ChatGPT is unfamiliar territory, here are some exercises to get you started.

I also put together a short, helpful guide on how to turn your know-how into an AI-friendly process.

Here are some other useful links I’ve gathered around learning to prompt better.