Case Study
AI Implementation Scoping Tool
The Problem
AI implementation projects fail at a predictable point: the gap between “we want to use AI” and “here's what to actually build.” Every AI engagement starts the same way — a business problem, a blank page, and a set of discovery questions that determine whether the project gets scoped correctly or spends months heading in the wrong direction.
The first deliverable of any implementation engagement is a scoping document. The same discovery structure applies every time: what's the current process, what systems and data exist, what does success look like. This is repeatable. I wanted to encode it as a tool — and demonstrate it working live.
What I Built
A single-page tool that runs a two-phase AI implementation discovery session. No login. No install. Open the URL, describe your problem, get a scoping document in under two minutes.
Prompt Architecture
Two prompts, two different jobs. Keeping them separate was a deliberate choice.
- —Question generation prompt: Instructs Claude to return a JSON array of exactly 3 strings — nothing else. The app validates the response shape before displaying anything. If the array doesn't have exactly 3 items, it errors and prompts a retry rather than showing a broken state.
- —Scoping document prompt: Specifies the exact markdown structure — section headers, table formats, phase names. Not to constrain Claude's reasoning, but to ensure output is immediately usable. A scoping document where sections move around is harder to act on. The format is opinionated because the use case is.
Design Decisions
- —Two prompts, not one: Combining question generation and document generation into a single prompt would trade reliability for brevity. Two clean system prompts, each with a single output job, produce more consistent results than one complex multi-purpose prompt.
- —Pre-generate all 3 questions at once: An adaptive approach — call Claude once per question, adjusting based on prior answers — would be more dynamic but means 3× the API calls and latency. Pre-generating from the initial problem description keeps question balance predictable (process / systems / success criteria) and loads in one round trip.
- —Vanilla JS, no framework: This tool has one job. Frameworks add build steps and deployment complexity for no real gain here. Vanilla JS is auditable (any developer reads app.js in 5 minutes), instantly deployable via Vercel, and forces clean state management — everything lives in one object.
- —Server-side API key: The Claude API key lives in a Vercel environment variable, never in the browser. A lightweight serverless function proxies requests. This means anyone can use the tool without needing their own key — and the architecture reflects how production AI tools are actually built.