AI‑Assisted Feature Briefs: 7 Prompts & a 1‑Page Schema That Produce Contractor‑Ready Specs

Deliver a single page that a contractor can open and start implementing. The one‑page schema bundles three machine‑readable outputs: (A) a minimal OpenAPI snippet (server URL, paths, request/response examples), (B) Figma slice requirements (frames, assets, exported layer names, export sizes), and (C) acceptance tests (Playwright / Cypress scenario names, steps, expected assertions). That combination makes both the design and dev work deterministic and reduces back‑and‑forth.

Structure the page as compact key/value sections: identity (name, owner, deadline), success criteria (measurable acceptance), API contract (OpenAPI YAML), UI slices (frame names, pixel sizes, interactions), and tests (test name, preconditions, steps, expected result). Include example values so contractors can validate the brief quickly and run the mock server immediately.

Below are seven purpose‑built prompts. Use them in sequence: (1) Context extractor, (2) Success criteria generator, (3) API contract generator, (4) UI slice specifier, (5) Acceptance test generator, (6) Risk & edge case enumerator, (7) Handoff checklist generator. Each prompt focuses an LLM on one concrete output to reduce scope creep and reduce folding too many responsibilities into a single pass.

When you run them, always prefix with a short context block (product, personas, constraints, out‑of‑scope). Keep each prompt strict about format—ask for YAML or JSON—so outputs are machine‑parsable and easy to validate.

Example snippets let contractors validate faster. For OpenAPI, include method, path, body schema and two example request/response pairs. For Figma, include explicit frame names and exact export names so design exports match the code. For tests, provide Playwright-style steps that map directly to UI interactions and API stubs.

Machine‑readable examples mean you can wire automated validations into CI: validate OpenAPI with an OpenAPI linter, run Figma export regressions, and execute acceptance tests against a mock server. This turns the brief into an executable contract rather than prose.

LLMs hallucinate when asked open, unconstrained questions. Use layered grounding and verification: (A) feed only canonical, short context (product doc, API catalog), (B) ask for outputs in strict JSON/YAML, (C) run an automated validator (schema linter, unit tests), and (D) human‑in‑the‑loop review for any invented IDs or undocumented endpoints. These steps shift the LLM from 'author' to 'formatter + enumerator'.

To stop scope creep, require that every feature brief include an explicit 'out of scope' list and a single 'success metric' that the contractor signs off on before work begins. If the LLM suggests additional features, capture them as backlog items rather than acceptance criteria.

Workshop the idea for 15–30 minutes and collect context (owner, deadline, constraints). Run the seven prompts in sequence, validate the OpenAPI snippet with a linter, export Figma slices (or request exact export names), and run the generated acceptance tests against a mocked server. Reserve one hour for a quick human review and a 30‑minute contractor kickoff—ideally the contractor should be able to run the mock server and pass at least one acceptance test during the kickoff.

Instrument the process: keep a brief version history, add a 'validated by' line for the human reviewer, and store the machine outputs in a repo. Over time, track which prompts required manual edits; shrink those by adjusting prompt wording and adding context documents to your RAG store.