Is it a mistake to start building an AI-powered client deliverable (like an HTML quarterly estimated tax payment report) and then try to back into a bigger database/app product, or should I approach it differently?

One member built an HTML-based estimated tax payment deliverable (similar to SafeSend) by prompting an LLM, and wanted to know if starting from a simple deliverable and working backward into a larger app/database was a sound approach or a mistake. The group's advice was to go for it, especially if there are many clients: put the deliverable on a web app rather than emailing a fixed HTML file to everyone, so each client gets their own page and the data can be maintained in a simple backing database. The recommended process is to first build a minimum viable product (MVP) and test it with one client (or yourself) to see what breaks. Before building, do a planning phase: have the model create a roadmap in a markdown file describing project stages and systems to be used, then run an adversarial review by feeding that markdown into a different model (e.g., write the plan in Claude, then have Gemini poke holes in it, then feed Gemini's critique back into Claude to refine the plan). This planning step was described as high-value because it's easy to accumulate 'tech debt' -- additional apps, databases, and systems that add overhead and maintenance burden -- if you just start building without a plan.