How do you write a spec-to-implementation prompt so an LLM won’t guess the hard parts?

Conclusion

Use a prompt that forces decisions into the spec before coding starts:

• explicit inputs/outputs (API/UI/data)
• non-negotiable constraints (security, latency, cost, runtime)
• edge cases + failure behavior
• acceptance tests (how we know it’s done)
• phased delivery with review gates

If the model is still “creative”, your spec is missing constraints.

Implementation examples may be available on DevSnips.

Explanation

In AI-driven development, most failures are not “bad code generation”. They are missing product/ops decisions:

• What is the source of truth? (DB vs cache vs API)
• What should happen on failure? (retry/backoff, partial success, UI states)
• What is allowed? (permissions, data exposure, rate limits)

A good prompt is not longer. It is more binding.

Practical Guide

Step 1: decide what you are actually delegating

If you want the model to implement end-to-end, your prompt must include:

• “what to build” (goal)
• “what not to break” (constraints)
• “how to judge output” (tests)

If you cannot write tests, delegate only a smaller slice.

Step 2: use this template (copy + fill)

Paste this, then fill the brackets.

You are a senior software engineer. Implement the feature described below.

## Goal (one sentence)
[What user value should be delivered?]

## Context
- Product: [what is this app?]
- Runtime: [Next.js / Node / Python / etc]
- Deploy: [Vercel / k8s / etc]

## Inputs
- [API requests, UI events, scheduled jobs]

## Outputs
- [DB writes, API responses, UI states, logs/metrics]

## Constraints (non-negotiable)
- Security: [auth rules, PII, secrets handling]
- Performance: [p95 latency budget, batch size]
- Cost: [token budget / caching rules]
- Reliability: [timeouts, retries, idempotency]

## Edge cases + failure behavior
- [case] -> [expected behavior]

## Acceptance criteria (tests)
- Unit tests:
  - [test name] -> [assertion]
- Integration tests:
  - [scenario] -> [expected behavior]

## Delivery plan
1) Phase 1: minimal vertical slice + tests
2) Phase 2: harden + metrics/logging
3) Phase 3: cleanup + docs

## Output format
- First: restate the design decisions you made (bullet list).
- Then: implement Phase 1 only.
- Then: list follow-up tasks for Phase 2/3.

Step 3: force the “decision summary” before code

The “restate decisions” section is the control surface. If it is wrong, you correct the spec before the model writes code.

Step 4: run a review gate per phase

Treat each phase like a PR:

• run tests
• check security boundaries
• check logging + rollback

Pitfalls

• “Make it nice” prompts: no constraints, no tests, no failure behavior
• No source of truth: the model invents state transitions
• One-shot implementation: hard to review, hard to roll back
• Spec diffs not communicated: the model is working from stale assumptions

Checklist

• [ ] Goal is a single sentence tied to user value
• [ ] Inputs are listed (events, requests, schedules)
• [ ] Outputs are listed (DB, response, UI, logs)
• [ ] Authn/authz rules are explicit
• [ ] PII/secrets policy is explicit (what must never be logged)
• [ ] Latency budget is stated (p95/p99)
• [ ] Token/cost budget is stated (if LLM is involved)
• [ ] Failure behavior is defined (timeouts, retries, partial success)
• [ ] Edge cases are enumerated (at least 5)
• [ ] Acceptance tests are listed (unit + integration)
• [ ] Delivery is phased with review gates
• [ ] Output format forces a decision summary before code

FAQ

Q1. Should I include my whole codebase in the prompt?

No. Provide only what changes the implementation decision: interfaces, data models, and constraints. Large dumps increase hallucination risk.

Q2. What if I don’t know the right constraints yet?

Then you should not delegate the full implementation. Delegate a spike: propose 2–3 design options with trade-offs, then pick one.

Q3. How do I know when the prompt is “good enough”?

When the model’s decision summary matches what a human reviewer would approve, and when acceptance tests are concrete and runnable.

Internal links

• Parent hub: AI development: start here
• Related:
  • Clarifying questions checklist
  • Remove AI-tells checklist
  • IDOR prevention checklist

References

• RFC 2119 (MUST/SHOULD language): https://www.rfc-editor.org/rfc/rfc2119

Disclaimer

Do not paste secrets into prompts.