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Requirements engineering is the control layer that turns an AI coding tool from a fast code generator into a predictable engineering assistant. Give the tool a written outcome, users, boundaries, repository context, examples, and testable acceptance criteria; ask it to restate those requirements and propose a plan before it edits anything. Then implement one small slice at a time, review the diff, run deterministic tests, inspect security and data handling, and record the change. This specification-first workflow limits ambiguity, exposes missing decisions early, and gives people a clear basis for approving or rejecting generated code.
Why does requirements engineering matter for AI coding tools?
An AI coding assistant predicts an implementation from the context you provide. If the context is a vague sentence such as “add team notifications,” the model must guess the users, event rules, permissions, delivery channels, failure behavior, and definition of done. A plausible-looking patch can therefore solve a different problem while appearing complete.
Requirements engineering replaces those guesses with an explicit source of truth. It connects the business or user outcome to scope, constraints, examples, architecture decisions, and verification. The assistant can plan against that source, while reviewers can judge whether the result satisfies an agreed requirement instead of debating whether the generated code “looks right.”
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What should a requirement baseline contain?
Start with a compact baseline for the feature or change. Put it in the issue, specification, or architecture decision record that the team will version with the code.
State the outcome and users
Describe the user or business result in observable terms: who needs what capability, in which workflow, and why. Identify user roles and trust boundaries. “A support agent can export the current queue” is more useful than “build an export feature,” because it implies a user, an object, and an action.
Define scope and non-scope
List the behaviors included in this change and the tempting adjacent behaviors that are deliberately excluded. Explicit non-goals prevent an assistant from adding screens, migrations, integrations, or refactors that were never approved.
Record assumptions, dependencies, and constraints
Capture assumptions about existing data, authentication, supported browsers or operating systems, deployment environments, and backward compatibility. Name dependencies such as an API contract, a database schema, or another ticket. Add constraints for language, framework, approved libraries, latency, licensing, and data residency.
Attach repository context
Point to the relevant directories, entry points, tests, coding conventions, configuration files, and architecture decisions. Explain which files may be changed and which are protected. GitHub’s guidance to “Start general, then get specific” works in practice when the general goal is followed by this concrete repository map.
Define success measures
Specify how the team will know the outcome was achieved. Measures can be behavioral (a report contains all records visible to the user), operational (a request fails safely when the upstream service times out), or quality-related (keyboard navigation works without a pointer). Avoid inventing a productivity or defect-reduction percentage; the reviewed guidance provides workflow controls, not a universal success-rate statistic.
How do I separate functional and quality requirements?
Functional requirements describe what the system does. Quality requirements describe the conditions under which it must do it. Keeping them separate stops an assistant from implementing the happy path while silently violating security, performance, accessibility, or maintainability expectations.
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| Requirement class | Questions to answer | Example |
|---|---|---|
| Functional behavior | What input, rule, output, and state change are required? | When an authorized agent selects a date range, the service returns matching tickets in pages. |
| Security and privacy | Who may perform the action? What data may cross a boundary? How are secrets handled? | Reject requests without the queue-read permission; never include customer secrets in logs. |
| Performance | What workload and response expectation apply, and how is it measured? | Define the request size, percentile, and environment rather than saying only “fast.” |
| Reliability and recovery | What happens on timeout, duplicate request, partial failure, or restart? | Use an idempotency key and report a retryable error when the export worker is unavailable. |
| Accessibility | Which users and assistive technologies must be supported? | All controls have an accessible name, visible focus, and keyboard-operable equivalent. |
| Maintainability | What structure, tests, documentation, and compatibility must be preserved? | Keep the existing service boundary, add unit and integration coverage, and document the new field. |
For each quality requirement, state the context and a verification method. “Secure” is not testable; “a user lacking permission receives a 403 and the event contains no payload data” is.
How can I make requirements and acceptance criteria testable?
Write each behavior so a reviewer can supply inputs and determine an expected result without guessing. Include normal, boundary, and failure cases.
Use an input–result–failure pattern
- Inputs: identify valid values, missing values, limits, roles, and preconditions.
- Expected result: specify the response, state change, emitted event, or user-visible message.
- Failure behavior: state the error class, whether data is rolled back, and whether retrying is safe.
- Acceptance check: describe the automated test, inspection, or demonstration that proves the criterion.
Resolve ambiguous words with examples
Terms such as “recent,” “large,” “active,” and “standard user” vary by team. Replace them with a date window, a numeric limit, a defined role, or a concrete example. If ordering matters, show ties and null values. If text is normalized, specify case, whitespace, locale, and Unicode behavior.
Example acceptance criteria
- Given an authenticated agent with queue-read permission, requesting page one with a page size of 50 returns at most 50 tickets and a continuation token when more exist.
- Given a user without that permission, the endpoint returns 403, performs no query for ticket contents, and writes an audit event without sensitive fields.
- Given an upstream timeout, the service returns the documented retryable error, does not create a partial export, and can safely accept the same idempotency key again.
- Given keyboard-only navigation, every filter, submit control, status message, and error can be reached and announced in the required sequence.
What should I put in a prompt for an AI coding assistant?
Give the assistant the baseline rather than paraphrasing it from memory. The prompt should identify its role, the source documents, the allowed scope, and the response format. OpenAI’s prompting guidance emphasizes identity, instructions, examples, and context; coding guidance from VS Code similarly calls for inputs, outputs, constraints, language, frameworks, libraries, structure, and expected behavior.
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Source of truth: docs/requirements/queue-export.md and ADR-042.
Goal: implement only the first export API slice described there.
Context: use the existing authorization middleware and pagination helper; do not add a new dependency.
Constraints: preserve the public API, redact sensitive fields in logs, and change only the listed files unless you explain why another file is required.
Before editing:
1. Restate the requirements and non-goals.
2. Map every acceptance criterion to planned code and tests.
3. List missing information, risks, and assumptions.
4. Propose a small sequence of edits.
Wait for approval. After approval, show the diff summary and the tests you ran.Ask the assistant to quote the requirement identifier beside each planned change. That creates a review trail and makes it easier to notice an unimplemented criterion. If the assistant cannot restate a rule accurately, resolve the specification before allowing code changes.
How should I implement AI-generated changes safely?
Implement one coherent slice at a time instead of requesting an entire application or cross-cutting rewrite. Small boundaries reduce context drift and make a wrong assumption cheap to discard.
- Choose a slice: select one endpoint, component, migration, or testable behavior with a clear acceptance boundary.
- Confirm the plan: compare the proposed files, dependencies, and data flow with the baseline. Reject unrequested refactors.
- Generate the patch: require the assistant to preserve existing conventions and explain any deviation.
- Inspect the diff immediately: look for unrelated edits, hidden configuration changes, generated files, permissive authorization, logging of payloads, and dependency changes.
- Run focused checks: execute the relevant unit, integration, type, lint, and formatting checks before moving to the next slice.
- Update the requirement status: mark criteria as implemented, blocked, or intentionally deferred, with links to the change and tests.
If a change requires a schema migration or public contract update, treat that as a separate slice with its own rollback and compatibility criteria. Do not let the assistant silently combine it with cosmetic cleanup.
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How do I review and test AI-written code?
Human review and deterministic checks are mandatory gates, not optional polish. The UK Home Office SEGAS-00020 standard states, “AI-assisted outputs MUST be reviewed and approved by a human before reaching production,” and, “You MUST test your code.” It also requires AI-assisted changes to remain traceable through engineering processes.
Review against the requirement baseline
- Trace every acceptance criterion to code and a test or inspection.
- Check authorization at the server-side boundary, not only in the interface.
- Inspect parsing, validation, encoding, error handling, retries, and idempotency.
- Verify that logs, telemetry, prompts, and test fixtures do not expose secrets or personal data.
- Check dependency licenses, versions, transitive packages, and whether the tool introduced an unapproved service.
- Confirm accessibility behavior with keyboard and assistive-technology checks appropriate to the product.
Test beyond the generated happy path
Run deterministic tests for boundaries, malformed input, unauthorized roles, concurrency, timeouts, duplicate requests, and recovery. Test the acceptance examples exactly as written, then add cases suggested by the risk assessment. An AI review can point out suspicious code, but it cannot replace executable tests, security analysis, or a person accountable for the release.
Use independent evidence
For high-impact changes, have a reviewer who did not write the prompt inspect the diff and test results. Keep the requirement, prompt or instruction reference, model or tool version, approvals, and test evidence with the issue or change record. This is especially important when a model or interface changes, because behavior and available controls can shift over time.
How should security, privacy, and governance constrain AI coding?
Decide what data an approved tool may receive before anyone pastes repository content into it. Classify source code, credentials, customer data, vulnerability details, and regulated information; redact or replace sensitive values in examples. Define whether prompts and outputs may be retained, whether external processing is allowed, and who can access them.
Governance should name approved tools, permitted autonomy, licensing rules, review roles, and escalation paths. OWASP SAMM recommends explicit policy for acceptable AI use, data sharing, autonomy, licensing, approved tools, and versioned AI-consumable instructions. NIST SP 800-218A extends the Secure Software Development Framework with generative-AI-specific practices across the software development life cycle; NIST published that material on July 26, 2024.
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Protect repository instruction files and policy documents from unauthorized edits. A malicious or accidental change to an instruction file can redirect future assistants, so require normal code review, ownership rules, and audit history for those files.
How do I preserve traceability and version the requirements?
Keep the requirement baseline, acceptance criteria, architecture decisions, and repository instructions in version control next to the code they govern. Reference stable identifiers in commits, pull requests, test names, and release notes. Record which portions were AI-assisted, which person reviewed them, the tool and model version when available, and the evidence used for approval.
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Update the specification in the same change that alters behavior. A stale document is worse than no document because it teaches the next assistant an obsolete contract. Protect changes to requirements with the same review and ownership controls as production code.
Which prompting and development approach should a team choose?
The approaches below differ mainly in how much ambiguity they leave for the model and how easily a reviewer can verify the result.
| Approach | Context supplied | Plan before edits | Review and verification | Best use |
|---|---|---|---|---|
| Vague feature request | Goal only; repository and constraints are implicit | Usually absent | Diff review is subjective; acceptance is unclear | Brainstorming or early discovery, not production implementation |
| Specification-first prompt | Versioned requirements, architecture context, examples, constraints, and acceptance criteria | Restatement, risk list, criterion-to-change map, then approval | Each criterion maps to code and deterministic checks | Most production feature work |
| Iterative bounded slices | The same baseline, narrowed to one coherent change | Short plan for each slice | Small diffs, focused tests, and fast rollback | Large features, unfamiliar code, or high-risk changes |
| Autonomous broad rewrite | Large context with many implied decisions | May produce an extensive plan but weak boundaries | High review cost and difficult fault isolation | Rarely justified; use only with exceptional controls |
Compare tools and practices on requirement clarity, repository-context quality, acceptance-criteria support, plan-before-edit behavior, diff visibility, deterministic test integration, security and privacy controls, traceability, model-version pinning, and governance over approved tools and data. A feature list alone cannot tell you which assistant is safer for your organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common failure modes?
The assistant solves the wrong problem
Cause: the prompt omits users, non-goals, or repository context. Fix: require a restatement and ask the assistant to identify contradictions before planning.
The happy path works but edge cases fail
Cause: requirements describe only successful input. Fix: add malformed, unauthorized, duplicate, timeout, boundary, and recovery cases with observable outcomes.
A secure feature leaks data
Cause: privacy and logging rules were treated as implementation details. Fix: state data boundaries, redaction rules, retention, and authorization checks as acceptance criteria, then inspect telemetry and fixtures.
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Cause: the request combines feature work, refactoring, dependency upgrades, and formatting. Fix: split the work into bounded slices and reject unrelated edits.
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Where can I learn more about requirements engineering?
Software Requirements, 3rd Edition by Karl Wiegers and Joy Beatty is a 672-page Microsoft Press edition dated August 15, 2013. It covers elicitation, analysis, specification, validation, management, agile projects, requirements tools, and risk management. Treat retailer availability and edition details as changeable, but the techniques remain a useful foundation for writing the baselines that AI tools need.
What should a team do first?
Choose one upcoming feature and write its outcome, users, scope, constraints, repository context, quality requirements, examples, and acceptance criteria. Have the assistant restate that document and produce a criterion-mapped plan without editing. Approve one small slice, review its diff, run the required tests and security checks, and record the evidence. Repeating that loop creates a durable source of truth instead of a collection of one-off prompts.
Frequently Asked Questions
Can an AI coding tool work from a user story alone?
It can draft ideas or a starting point, but a user story rarely defines permissions, data boundaries, failure behavior, repository conventions, or test evidence. Add those details before asking for production code, and treat the story as the outcome rather than the complete contract.
Should requirements live in the issue tracker or the repository?
Use the issue for discussion and workflow, but keep the approved specification, acceptance criteria, and architecture decisions versioned with the code. The repository copy gives future prompts a stable, reviewable source when tickets are edited or archived.
Do I need to pin the AI model version?
Pinning is useful when repeatability, auditability, or regulated delivery matters because model behavior can change. Record the model and tool version when available, and maintain evaluation tests so an upgrade can be compared with the approved behavior.
How should I handle generated database migrations?
Review the migration as a production change: verify locking and runtime impact, backward compatibility, rollback or forward-fix plans, data classification, and behavior during mixed application versions. Run it against representative data or a safe copy before deployment, even if the assistant generated accompanying tests.
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