Prompt Like a PM: What is Prompt Engineering?
All prompt engineering is — is just thinking and talking like a PM (Product Manager).
Generative AI tools (chat gpt, deepseek, etc) are still just software — built by product/tech teams who follow structured, repeatable systems.
There’s almost always a PM — the person translating high level strategy/goals into buildable features, writing user stories, aligning the dev team on what matters most. And the best PMs are often the best communicators in tech. They translate business needs into language engineers understand.
Knowing this…what if we started treating our prompts the same way a PM writes product specs or user stories?
Approaching prompt engineering with the same level of clarity and intent we’d use when writing a clear brief for a designer, or submitting a detailed, well-scoped tech support ticket for an engineer?
Why does this matter?
💡 Because I don’t need to be technical to use AI well.
What makes good prompting comes from Structured Thinking:
Clear goals.
Defined roles.
Relevant context.
Enough specificity to get the result i actually need (so I save time, reduce iterations)
The same principles that make product managers effective — clarity, structure, intention — make prompts powerful too.
On it’s own, AI doesn’t have vision. It doesn’t know the “why” behind my work, or the consequences of the output.
It’s powerful, but not purposeful.
Generative AI isn’t creative or strategic on its own.
It reflects the clarity of the input — the thinking behind the prompt.
Prompting like a PM — with intent, structure, edge cases, context, means im bringing my human advantage to the table: judgment, context, nuance, lived experience.
So when I compare prompt writing to things many non-engineers already do — like writing a clear brief, outlining a to-do list, or drafting a product spec — it lowers the barrier to entry.
This makes generative AI feel less like a technical skill, and more like an extension of everyday thinking.
TLDR: Same skills, new tool.
