Vibe coding with product discipline

AI-assisted development has made it dramatically easier to turn an idea into working software. But speed creates a new problem: a prototype can be built in hours while the thinking behind it (customer problem, requirements, business rules, validation, and technical decisions) stays scattered across prompts, conversations, documents, and someone's memory. That creates a dangerous gap between "I built something" and "we have a product." Process OS was my exploration of a simple question: what would the product development process look like if AI helped maintain the structure around the product, not just generate the code?

Traditional product development creates artifacts deliberately: research, requirements, briefs, acceptance criteria, architecture decisions. AI development often reverses that process. A builder describes an idea, AI generates an interface, the builder reacts, another prompt changes the workflow, another changes the data model. Within a few iterations a surprisingly sophisticated product exists, but the documentation explaining why it works the way it does often doesn't.

• Product decisions become buried inside prompt history.
• Requirements evolve without a clear source of truth.
• Validation happens informally instead of systematically.
• Engineering teams inherit prototypes without sufficient context.
• AI-generated code can move faster than organizational understanding.
• Teams struggle to distinguish experimental behavior from intentional product requirements.

Product manager, product designer, and builder. I designed the framework, built the working product with Lovable, Claude Code, Codex, and Supabase, and created every artifact the system prescribes.

1. 1.

   Vibe Coding Problem & Live Product

   The starting point captures the problem being solved alongside the working application. That establishes an important distinction: the prototype demonstrates a possible solution, but the problem definition remains the anchor.

2. 2.

   Validation Brief

   A structured look at the assumptions behind the product: target customer, pain points, value proposition, assumptions, risks, evidence, and questions that still need validation. The objective is to prevent prototype velocity from being mistaken for product-market validation.

3. 3.

   Living PRD

   Instead of a static requirements document written before development begins, the PRD evolves alongside the software. It stays the durable source of truth for product goals, user problems, workflows, functional requirements, business rules, edge cases, success criteria, and future considerations, enabling rapid experimentation without abandoning product discipline.

4. 4.

   Prompt Library & Logic

   Prompts are becoming part of the development environment, so Process OS treats them that way. Important prompts, system logic, implementation decisions, and behavioral rules are captured so future builders can understand how the product was constructed and continue it without reconstructing its history.

5. 5.

   Documentation that emerges from the work

   My guiding principle: documentation should emerge from the work instead of becoming additional work. Each artifact informs the next: define the problem, test the assumptions, build the product, capture what changed, prepare the handoff. The prototype itself became part of the research process, with Lovable, Claude Code, Codex, and Supabase letting me move rapidly between interface design, product logic, implementation, and iteration while continuously refining the product definition.

6. 6.

   From prototype to engineering handoff

   The final stage answered a question that increasingly matters: what would another engineer need to confidently take over this product tomorrow? The handoff documented product architecture, major workflows, application logic, data considerations, dependencies, known limitations, implementation assumptions, and future opportunities. The goal was not simply to make the application work; it was to make the product transferable.

• The bottleneck is shifting from building software to maintaining clarity. AI can increasingly produce interfaces, workflows, code, and documentation. But teams still need to answer: who is this for, what problem are we solving, which assumptions are actually validated, why does the product behave this way, and what would another team need to continue the work?
• Strong product thinking matters more, not less. AI dramatically reduces the cost of experimentation. That makes judgment the differentiator.
• Living documentation beats static documentation. A PRD written months before implementation becomes obsolete. Requirements should evolve with the product as a continuously updated representation of current intent.
• Human decisions, AI acceleration. AI can synthesize information, generate specifications, and accelerate implementation, but product judgment remains human. The system was designed to make product decisions easier to see, not to hide them behind automation.
• Engineer for handoff. A successful prototype shouldn't depend indefinitely on the person who originally prompted it into existence. Handoff is a first-class product outcome, not an afterthought.