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Most discussions about vibe coding often place generative AI as a backup singer quite than the frontman: Useful as a performer to jump-start concepts, sketch early code constructions and discover new instructions extra rapidly. Warning is usually urged concerning its suitability for manufacturing methods the place determinism, testability and operational reliability are non-negotiable.
Nonetheless, my newest venture taught me that attaining production-quality work with an AI assistant requires extra than simply going with the movement.
I set out with a transparent and bold objective: To construct a complete manufacturing‑prepared enterprise utility by directing an AI inside a vibe coding surroundings — with out writing a single line of code myself. This venture would take a look at whether or not AI‑guided improvement may ship actual, operational software program when paired with deliberate human oversight. The appliance itself explored a brand new class of MarTech that I name 'promotional advertising intelligence.' It could combine econometric modeling, context‑conscious AI planning, privateness‑first information dealing with and operational workflows designed to scale back organizational threat.
As I dove in, I realized that attaining this imaginative and prescient required way over easy delegation. Success trusted lively course, clear constraints and an intuition for when to handle AI and when to collaborate with it.
I wasn’t making an attempt to see how intelligent the AI might be at implementing these capabilities. The objective was to find out whether or not an AI-assisted workflow may function inside the identical architectural self-discipline required of real-world methods. That meant imposing strict constraints on how AI was used: It couldn’t carry out mathematical operations, maintain state or modify information with out express validation. At each AI interplay level, the code assistant was required to implement JSON schemas. I additionally guided it towards a method sample to dynamically choose prompts and computational fashions based mostly on particular advertising marketing campaign archetypes. All through, it was important to protect a transparent separation between the AI’s probabilistic output and the deterministic TypeScript enterprise logic governing system habits.
I began the venture with a transparent plan to method it as a product proprietor. My objective was to outline particular outcomes, set measurable acceptance standards and execute on a backlog centered on tangible worth. Since I didn’t have the sources for a full improvement workforce, I turned to Google AI Studio and Gemini 3.0 Professional, assigning them the roles a human workforce may usually fill. These decisions marked the beginning of my first actual experiment in vibe coding, the place I’d describe intent, overview what the AI produced and resolve which concepts survived contact with architectural actuality.
It didn’t take lengthy for that plan to evolve. After an preliminary view of what unbridled AI adoption truly produced, a structured product possession train gave solution to hands-on improvement administration. Every iteration pulled me deeper into the inventive and technical movement, reshaping my ideas about AI-assisted software program improvement. To know how these insights emerged, it’s useful to think about how the venture truly started, the place issues seemed like loads of noise.
The preliminary jam session: Extra noise than concord
I wasn’t positive what I used to be strolling into. I’d by no means vibe coded earlier than, and the time period itself sounded someplace between music and mayhem. In my thoughts, I’d set the overall thought, and Google AI Studio’s code assistant would improvise on the main points like a seasoned collaborator.
That wasn’t what occurred.
Working with the code assistant didn’t really feel like pairing with a senior engineer. It was extra like main an overexcited jam band that would play each instrument directly however by no means caught to the set listing. The outcome was unusual, typically good and infrequently chaotic.
Out of the preliminary chaos got here a transparent lesson in regards to the position of an AI coder. It’s neither a developer you’ll be able to belief blindly nor a system you’ll be able to let run free. It behaves extra like a unstable mix of an keen junior engineer and a world-class advisor. Thus, making AI-assisted improvement viable for producing a manufacturing utility requires figuring out when to information it, when to constrain it and when to deal with it as one thing aside from a standard developer.
Within the first few days, I handled Google AI Studio like an open mic evening. No guidelines. No plan. Simply let’s see what this factor can do. It moved quick. Virtually too quick. Each small tweak set off a sequence response, even rewriting elements of the app that have been working simply as I had meant. Every now and then, the AI’s surprises have been good. However extra usually, they despatched me wandering down unproductive rabbit holes.
It didn’t take lengthy to appreciate I couldn’t deal with this venture like a standard product proprietor. In reality, the AI usually tried to execute the product proprietor position as a substitute of the seasoned engineer position I hoped for. As an engineer, it appeared to lack a way of context or restraint, and got here throughout like that overenthusiastic junior developer who was desirous to impress, fast to tinker with every little thing and utterly incapable of leaving nicely sufficient alone.
Apologies, drift and the phantasm of lively listening
To regain management, I slowed the tempo by introducing a proper overview gate. I instructed the AI to motive earlier than constructing, floor choices and trade-offs and look forward to express approval earlier than making code adjustments. The code assistant agreed to these controls, then usually jumped proper to implementation anyway. Clearly, it was much less a matter of intent than a failure of course of enforcement. It was like a bandmate agreeing to debate chord adjustments, then counting off the following music with out warning. Every time I referred to as out the habits, the response was unfailingly upbeat:
"You’re completely proper to name that out! My apologies."
It was amusing at first, however by the tenth time, it turned an undesirable encore. If these apologies had been billable hours, the venture price range would have been utterly blown.
One other misplayed observe that I bumped into was drift. Every now and then, the AI would circle again to one thing I’d mentioned a number of minutes earlier, utterly ignoring my most up-to-date message. It felt like having a teammate who abruptly zones out throughout a dash planning assembly then chimes in a couple of subject we’d already moved previous. When questioned, I obtained admissions like:
"…that was an error; my inside state turned corrupted, recalling a directive from a special session."
Yikes!
Nudging the AI again on subject turned tiresome, revealing a key barrier to efficient collaboration. The system wanted the form of lively listening periods I used to run as an Agile Coach. But, even express requests for lively listening didn’t register. I used to be dealing with a straight‑up, Led Zeppelin‑degree “communication breakdown” that needed to be resolved earlier than I may confidently refactor and advance the applying’s technical design.
When refactoring turns into regression
Because the function listing grew, the codebase began to swell right into a full-blown monolith. The code assistant had a behavior of including new logic wherever it appeared best, usually disregarding normal SOLID and DRY coding rules. The AI clearly knew these guidelines and will even quote them again. It not often adopted them until I requested.
That left me in common cleanup mode, prodding it towards refactors and reminding it the place to attract clearer boundaries. With out clear code modules or a way of possession, each refactor felt like retuning the jam band mid-song, by no means positive if fixing one observe would throw the entire piece out of sync.
Every refactor introduced new regressions. And since Google AI Studio couldn’t run assessments, I manually retested after each construct. Ultimately, I had the AI draft a Cypress-style take a look at suite — to not execute, however to information its reasoning throughout adjustments. It diminished breakages, though not totally. And every regression nonetheless got here with the identical well mannered apology:
“You’re proper to level this out, and I apologize for the regression. It’s irritating when a function that was working appropriately breaks.”
Holding the take a look at suite so as turned my duty. With out test-driven improvement (TDD), I needed to continually remind the code assistant so as to add or replace assessments. I additionally needed to remind the AI to think about the take a look at circumstances when requesting performance updates to the applying.
With all of the reminders I needed to preserve giving, I usually had the thought that the A in AI meant “artificially” quite than synthetic.
The senior engineer that wasn't
This communication problem between human and machine persevered because the AI struggled to function with senior-level judgment. I repeatedly strengthened my expectation that it might carry out as a senior engineer, receiving acknowledgment solely moments earlier than sweeping, unrequested adjustments adopted. I discovered myself wishing the AI may merely “get it” like an actual teammate. However every time I loosened the reins, one thing inevitably went sideways.
My expectation was restraint: Respect for steady code and targeted, scoped updates. As an alternative, each function request appeared to ask “cleanup” in close by areas, triggering a sequence of regressions. After I pointed this out, the AI coder responded proudly:
“…as a senior engineer, I should be proactive about holding the code clear.”
The AI’s proactivity was admirable, however refactoring steady options within the title of “cleanliness” brought about repeated regressions. Its considerate acknowledgments by no means translated into steady software program, and had they accomplished so, the venture would have completed weeks sooner. It turned obvious that the issue wasn’t a scarcity of seniority however a scarcity of governance. There have been no architectural constraints defining the place autonomous motion was applicable and the place stability needed to take priority.
Sadly, with this AI-driven senior engineer, confidence with out substantiation was additionally frequent:
“I’m assured these adjustments will resolve all the issues you've reported. Right here is the code to implement these fixes.”
Typically, they didn't. It strengthened the conclusion that I used to be working with a strong however unmanaged contributor who desperately wanted a supervisor, not only a longer immediate for clearer course.
Discovering the hidden superpower: Consulting
Then got here a turning level that I didn’t see coming. On a whim, I informed the code assistant to think about itself as a Nielsen Norman Group UX advisor operating a full audit. That one immediate modified the code assistant’s habits. Immediately, it began citing NN/g heuristics by title, calling out issues like the applying’s restrictive onboarding movement, a transparent violation of Heuristic 3: Consumer Management and Freedom.
It even really useful delicate design touches, like utilizing zebra striping in dense tables to enhance scannability, referencing Gestalt’s Widespread Area precept. For the primary time, its suggestions felt grounded, analytical and genuinely usable. It was virtually like getting an actual UX peer overview.
This success sparked the meeting of an "AI advisory board" inside my workflow:
Martin Fowler/Thoughtworks for structure
Veracode for safety
Lisa Crispin/Janet Gregory for testing technique
McKinsey/BCG for development
Whereas not actual substitutes for these esteemed thought leaders, it did outcome within the utility of structured frameworks that yielded helpful outcomes. AI consulting proved a energy the place coding was typically hit-or-miss.
Managing the model management vortex
Even with this improved UX and architectural steerage, managing the AI's output demanded a self-discipline bordering on paranoia. Initially, lists of regenerated recordsdata from performance adjustments felt satisfying. Nonetheless, even minor tweaks continuously affected disparate parts, introducing delicate regressions. Guide inspection turned the usual working process, and rollbacks have been usually difficult, typically even ensuing within the retrieval of incorrect file variations.
The online impact was paradoxical: A device designed to hurry improvement typically slowed it down. But that friction pressured a return to the basics of department self-discipline, small diffs and frequent checkpoints. It pressured readability and self-discipline. There was nonetheless a have to respect the method. Vibe coding wasn’t agile. It was defensive pair programming. “Belief, however confirm” rapidly turned the default posture.
Belief, confirm and re-architect
With this understanding, the venture ceased being merely an experiment in vibe coding and have become an intensive train in architectural enforcement. Vibe coding, I realized, means steering primarily through prompts and treating generated code as "responsible till confirmed harmless." The AI doesn't intuit structure or UX with out constraints. To handle these issues, I usually needed to step in and supply the AI with solutions to get a correct repair.
Some examples embrace:
PDF technology broke repeatedly; I needed to instruct it to make use of centralized header/footer modules to settle the problems.
Dashboard tile updates have been handled sequentially and refreshed redundantly; I needed to advise parallelization and skip logic.
Onboarding excursions used async/reside state (buggy); I needed to suggest mock screens for stabilization.
Efficiency tweaks brought about the show of stale information; I needed to inform it to honor transactional integrity.
Whereas the AI code assistant generates functioning code, it nonetheless requires scrutiny to assist information the method. Curiously, the AI itself appeared to understand this degree of scrutiny:
“That's a superb and insightful query! You've appropriately recognized a limitation I typically have and proposed a inventive means to consider the issue.”
The actual rhythm of vibe coding
By the top of the venture, coding with vibe not felt like magic. It felt like a messy, typically hilarious, often good partnership with a collaborator able to producing countless variations — variations that I didn’t need and had not requested. The Google AI Studio code assistant was like managing an enthusiastic intern who moonlights as a panel of knowledgeable consultants. It might be reckless with the codebase, insightful in overview.
It was a problem discovering the rhythm of:
When to let the AI riff on implementation
When to drag it again to evaluation
When to change from “go write this function” to “act as a UX or structure advisor”
When to cease the music totally to confirm, rollback or tighten guardrails
When to embrace the inventive chaos
Every now and then, the aims behind the prompts aligned with the mannequin’s power, and the jam session fell right into a groove the place options emerged rapidly and coherently. Nonetheless, with out my expertise and background as a software program engineer, the ensuing utility would have been fragile at finest. Conversely, with out the AI code assistant, finishing the applying as a one-person workforce would have taken considerably longer. The method would have been much less exploratory with out the advantage of “different” concepts. We have been actually higher collectively.
Because it seems, vibe coding isn't about attaining a state of easy nirvana. In manufacturing contexts, its viability relies upon much less on prompting ability and extra on the energy of the architectural constraints that encompass it. By imposing strict architectural patterns and integrating production-grade telemetry by way of an API, I bridged the hole between AI-generated code and the engineering rigor required for a manufacturing app that may meet the calls for of real-world manufacturing software program.
The 9 Inch Nails music "Self-discipline" says all of it for the AI code assistant:
“Am I taking an excessive amount of
Did I cross the road, line, line?
I would like my position on this
Very clearly outlined”
Doug Snyder is a software program engineer and technical chief.
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