Follow one tiny change from acceptance criteria through release evidence.
You will turn an idea into a scoped, tested increment while using AI for alternatives and drafts—not unreviewed authority.
Before you begin
Start with a small idea, version-controlled workspace, and test environment separated from production accounts and data.
Define the problem
Write who the application serves, the task it improves, the data it handles, and what the first version will not do. Turn that into observable acceptance criteria. A clear boundary gives both humans and AI assistants a stable target.
Build a thin vertical slice
Create the smallest path from input to useful output. Ask AI for alternatives and tradeoffs, then choose an architecture you can maintain. Generate small patches, keep dependencies minimal, and verify licenses and package names before installation.
Test beyond the happy path
Add unit tests for logic, integration tests for boundaries, and manual checks for usability. Test empty input, large input, offline behavior, permission denial, and recovery after interruption. For mobile or hardware-dependent software, use a real device as well as an emulator.
Release with evidence
Document supported behavior and known limitations. Build from a clean source checkout, record versions and hashes, scan for secrets, and preserve a rollback artifact. AI can help draft release notes, but test results—not confidence—determine readiness.
Write testable stories
Describe the user, problem, and observable outcome. Write stories such as “Given a note, when I request categories, then dates remain unchanged.” Add non-goals and data boundaries. Ask AI to find ambiguity and edge cases, then decide yourself. This document becomes the reference for architecture, implementation, and acceptance rather than allowing each prompt to redefine the product.
Build a thin application safely
Choose a familiar stack and few dependencies. Implement one path from input to visible output. Use AI for a draft interface, tests, or a small function, then inspect the diff. Verify APIs in official docs and packages in the official registry. Keep secrets out and use fake data. Commit a working checkpoint before risky refactoring.
Verify beyond the happy path
Unit-test deterministic logic and integration-test files, networks, and databases. For AI output, test representative examples, source support, refusals, and failure behavior. Manually complete the user story on target devices and browser sizes. Test empty, large, malformed, offline, denied-permission, and interrupted cases. Record versions and commands. A polished demo is not evidence of recovery or edge-case quality.
Release and maintain deliberately
Scan source and artifacts for secrets, review dependencies and licenses, and document behavior and limits. Require approval for deployment, signing, publishing, migrations, or billing. Preserve source hashes and rollback. Re-run acceptance tests when changing model, prompt, dependency, or runtime because any can alter behavior without a visible interface change.
Choose tools by development phase
Decision: Use assistants for scoped work and deterministic build/test tools for release evidence.
Relevant options: GitHub Copilot, Cursor, Continue, and Aider support different editor or terminal workflows. Repository policy determines whether local or hosted endpoints are eligible.
Worked example: Turn one story into acceptance criteria, implement the smallest slice on a branch, test, and package from clean source.
Failure mode: The assistant may invent an API, add a dependency, weaken a test, or expand into deployment.
Validation: Review architecture and licenses, run static checks and tests, complete the user path, and compare artifacts to source.
Useful next step: Keep planning, implementation, verification, and release as separate gates.
Official sources checked 2026-10-24: GitHub Copilot · Cursor · Continue · Aider. Product availability, support, terms, pricing, and licenses can change. These are vendor or project sources; no independent benchmark is claimed.
End-to-end case: validated clamp utility
The disposable clamp case defines a risk, acceptance criteria, one implementation change, tests, patch review, and retained command output.
Evidence covers only the pure function under Node 26.8.1. Packaging, browser integration, accessibility, wider security, and deployment are NOT_TESTED.
Authoritative references
Reference scope checked 2026-10-27. These sources support the concepts and risk controls; product-specific behavior still requires the nearby vendor documentation and a dated test.
Your next step
Write one sentence of user value and three acceptance checks, then require evidence for each.
Editorial provenance
Nyfir Studios is the publisher and editorially responsible organization. AI tools assisted with drafting and structured review for this candidate; factual claims, test labels, links, and retained evidence require human review before deployment. No named employee, credential, or unrecorded hands-on result is implied.