The term “prompt engineering” got turned into something of a punchline fairly quickly — there ended up being far more people selling courses and threads about supposedly magic phrasing tricks than there were people actually demonstrating what it looks like inside a real, working developer’s ordinary Tuesday. So I want to walk through the plain, genuinely unglamorous version of this, from someone who does it daily as an actual part of running a development agency and isn’t selling anything related to it.
It’s overwhelmingly about context, not clever phrasing
The single biggest jump in output quality I’ve gotten from working with AI coding tools didn’t come from discovering some clever phrase or magic word ordering. It came from consistently giving the tool the actual, specific constraints of the project upfront before asking for anything — the exact naming convention I use for a particular client’s codebase, such as the cayk_ prefix I use for CAYK Marketing projects versus bixily_ or kag_ for other clients; whether the project is built on ACF Pro or native WordPress meta boxes, and why that decision was made for this specific project; what browser support genuinely matters for this specific site’s actual visitor base, rather than a generic “support all browsers” assumption that leads to over-engineered solutions for edge cases that don’t apply.
A vague prompt reliably gets back a generic, technically correct but practically unusable answer — code that would work fine in a blank-slate project but doesn’t fit the actual conventions and constraints of the codebase it needs to live in. A prompt loaded with the real project context gets back something I can genuinely drop in with minimal editing, because it was generated against the actual constraints rather than a generic best-practices template that happens to be technically accurate but practically wrong for this specific situation.
The single habit that’s changed the most about how I work
I stopped asking for finished, complete code on the first pass for anything beyond genuinely trivial tasks. My workflow now is to ask for the proposed approach first — something like “how would you structure this feature given these constraints” — read through that proposed approach carefully, push back explicitly on whatever part of it I disagree with or that doesn’t fit the project, and only then ask for the actual implementation once the approach itself is something I’d have chosen. This sounds like it adds an extra step to every task, and it does, but that extra step reliably catches bad architectural decisions before they’re baked into three hundred lines of working code that I’d otherwise have to unwind and rewrite later once the flaw becomes apparent. It’s considerably cheaper, in both time and frustration, to redirect a proposed plan in two sentences than to rewrite an entire file after discovering halfway through testing that the underlying approach was wrong for this specific project.
A concrete example of this two-step approach in practice
Working on the AuditFlow AI platform recently, I needed a caching layer for repeated PageSpeed API calls against the same URL within a short window, to avoid burning through rate limits unnecessarily. Asking directly for implementation code on the first pass would likely have produced a reasonable but generic in-memory caching solution. Asking for the approach first surfaced a genuinely better structural question I hadn’t fully considered upfront: should this cache live at the application layer, or should it be pushed down to a database-backed layer that survives a server restart, given that the platform is meant to run continuously in production. That’s exactly the kind of architectural decision that’s meaningfully cheaper to get right at the planning stage than to discover was wrong after the caching layer was already built and integrated throughout the codebase.
What I still do entirely by hand, without exception
Anything involving a specific, concrete visual bug that I can only actually diagnose by looking at the rendered page in a real browser — a subtle layout shift that only appears at a particular viewport width, an animation timing issue that only shows up on a lower-powered device, a font rendering slightly differently on one specific combination of device and browser. No amount of careful prompt refinement replaces actually opening browser dev tools and looking directly at the real, rendered problem. The genuine skill here isn’t learning to ask cleverer questions of an AI tool instead of thinking through a problem yourself. It’s knowing precisely which parts of the job still fundamentally require you to look, test, and think, and confidently outsourcing everything else that doesn’t.
The version of this worth actually learning
If you’re a developer and the term “prompt engineering” has mostly registered as marketing noise to you, I’d reframe it entirely as this: it’s the discipline of front-loading real project context before asking for output, and separating the “what approach should we take” question from the “now write the code” question instead of collapsing both into a single request. That’s the entire practical substance of it, and it’s a habit worth building deliberately rather than a skill you either happen to have a knack for or don’t.
