AI-Assisted Prototype Build Record

AI-Assisted Prototype Build Record

Prototype boundary

An AI-assisted prototype is a controlled learning artifact. It should not begin with real customer data, production credentials, payment capability, regulated decisions, public write access, or infrastructure authority.

The lowest-risk example is local or private, uses synthetic data, has one narrow user job, and can be deleted without harming a user or business process.

Build loop

Replit's checkpoint documentation explains the product's recovery surface. A checkpoint can restore state; it does not prove that the saved state is correct, secure, or production ready.

https://docs.replit.com/features/version-control/checkpoints-and-rollbacks

Build record

Preserve the date, model, product surface, plan or account tier, prompts, attachments, source revision, generated files, human edits, installed packages, lockfile changes, commands, tests, manual observations, security findings, costs, errors, checkpoints, and final disposition.

The record should also say what the system did not test. A passing browser preview does not test another browser, keyboard access, screen-reader output, concurrency, data recovery, dependency compromise, rate limits, or a production environment.

Failed acceptance as useful evidence

A failed test is not an interruption to the prototype. It is evidence that the brief, model output, implementation, or understanding needs revision.

E057's missing Pong opponent is the useful moment. The desired behavior existed in the builder's head but not in the artifact. The builder noticed the gap, described it, and requested a correction. A prototype workflow should make that mismatch cheap and visible.

Data and permission boundary

Use fabricated names, addresses, records, tokens, and transactions. Test credentials must have no value outside the prototype. Network access should be restricted to required sources. Writes should remain local or in an isolated disposable environment.

Any move into personal, confidential, proprietary, regulated, financial, health, safety, or customer data requires an authorized environment, data owner, privacy review, retention rule, access control, and incident path.

Outcome

A successful prototype produces learning, not release permission. Its handoff should name the maturity stage and list what must be established before a proof of concept, pilot, beta, or production release.