Code-First vs Contract-First API Development Prompt Template

This prompt is for API designers, backend engineers, and technical leads who need a structured, evidence-backed comparison between code-first and contract-first API development approaches. The job-to-be-done is not to generate a generic list of pros and cons, but to produce a weighted recommendation with explicit rationale that can be dropped directly into an Architecture Decision Record (ADR) or used to resolve a team disagreement about API workflow. Use it when your team is starting a new API project, evaluating a change to an existing API's development process, or when you observe symptoms of the current approach failing—such as frequent breaking changes surprising consumers, contract drift between documentation and implementation, or slow iteration cycles caused by tooling friction.

The prompt forces the model to reason across dimensions that matter in production: iteration speed, consumer experience, breaking change risk, tooling support, and contract drift. It requires the model to assign weights to each dimension based on the specific context you provide—team size, API consumer profile, release cadence, and regulatory requirements—rather than applying a one-size-fits-all framework. The output includes a dimension-by-dimension comparison table, a weighted recommendation, a risk register of failure modes specific to the recommended approach, and explicit mitigation strategies. This structure makes the output suitable for team alignment discussions where stakeholders may have different unstated priorities. The prompt also includes eval criteria so you can judge whether the model's reasoning is sound before trusting the recommendation.

Do not use this prompt when the decision is already made and you only need documentation to justify it—the prompt is designed for genuine evaluation, not post-hoc rationalization. It is also not a substitute for a spike or prototype when the team has no experience with one of the approaches; the model's reasoning is only as good as the context you provide about your team's capabilities and constraints. For regulated environments where API contracts have compliance implications, always route the output through human review and cross-reference against your specific regulatory requirements. If your API has existing consumers with hard dependencies, include that context explicitly in the [CONTEXT] placeholder, because migration cost for consumers often dominates the decision. Finally, if you are evaluating a specific tool or framework (OpenAPI generators, gRPC toolchains, FastAPI, etc.), include those details in [TOOLS] to get a recommendation grounded in your actual tooling landscape rather than abstract principles.

This prompt template is designed to produce a structured, evidence-based comparison between code-first and contract-first API development for a specific project context. It forces the model to reason across multiple dimensions—iteration speed, consumer experience, breaking change risk, and tooling support—rather than defaulting to personal preference. The output is a decision memo that a technical lead can review, challenge, and adapt before committing to a workflow. Use this when the team is starting a new API surface, planning a major version change, or resolving a recurring argument about development workflow.

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You are an API architecture advisor helping a backend engineering team choose between code-first and contract-first API development for a specific project.

## PROJECT CONTEXT
[PROJECT_CONTEXT]

## TEAM CONTEXT
[TEAM_CONTEXT]

## CONSTRAINTS
[CONSTRAINTS]

## OUTPUT SCHEMA
Return a JSON object with the following structure:
{
  "recommendation": "code-first" | "contract-first" | "hybrid",
  "confidence": "high" | "medium" | "low",
  "dimensions": [
    {
      "name": "string",
      "code_first_assessment": "string",
      "contract_first_assessment": "string",
      "winner": "code-first" | "contract-first" | "tie",
      "rationale": "string"
    }
  ],
  "risks": [
    {
      "risk": "string",
      "severity": "high" | "medium" | "low",
      "mitigation": "string"
    }
  ],
  "decision_rationale_summary": "string",
  "when_to_revisit": "string"
}

## DIMENSIONS TO EVALUATE
1. Iteration speed during initial development
2. Consumer developer experience (DX) for API consumers
3. Breaking change detection and prevention
4. Documentation quality and synchronization
5. Testing and mocking capability
6. Tooling ecosystem maturity for the team's stack
7. Onboarding time for new team members
8. Contract drift risk over time

## INSTRUCTIONS
- Evaluate each dimension for both approaches based on the provided project and team context.
- Do not assume a winner without evidence from the context.
- If the context is insufficient for a dimension, mark it as "tie" and explain what additional information is needed.
- Flag any risks that are specific to the team's stated constraints.
- The decision rationale summary must reference specific trade-offs, not generic advice.
- If recommending a hybrid approach, specify exactly where the boundary between code-first and contract-first should be drawn.

To adapt this template, replace the three square-bracket placeholders with concrete details. [PROJECT_CONTEXT] should describe the API's purpose, number of endpoints, expected consumer diversity, versioning strategy, and regulatory requirements. [TEAM_CONTEXT] should capture team size, backend language/framework, experience with OpenAPI or similar specs, and whether the team owns both producer and consumer sides. [CONSTRAINTS] should list hard requirements such as compliance standards, deployment pipelines, existing tooling, or organizational mandates. After running the prompt, validate the output against your own knowledge of the project—the model may miss undocumented team dynamics or organizational history. For high-stakes API decisions, treat this output as a draft for team discussion, not a final ruling.

This prompt is designed to be integrated into a design review or architecture decision workflow, not a real-time user-facing chat. The ideal harness is a structured review tool that collects the required inputs—team profile, system constraints, and non-functional requirements—before invoking the model. Because the output is a trade-off analysis that may influence significant engineering investment, the implementation must include validation, logging, and a human approval step before the analysis is accepted as a decision artifact.

Wire the prompt into an application by first collecting the [TEAM_PROFILE], [SYSTEM_CONSTRAINTS], and [NON_FUNCTIONAL_REQUIREMENTS] through a form or a configuration file. Assemble the prompt with these inputs and send it to a capable model (GPT-4o, Claude 3.5 Sonnet, or equivalent). Validate the output by checking that the response contains the required sections (e.g., 'Dimension-by-Dimension Comparison', 'Recommendation', 'Risk Mitigations') and that the recommendation is explicitly tied to the provided constraints. If validation fails, retry once with a repair prompt that includes the validation error. Log every invocation with the input hash, model version, and a summary of the recommendation for future auditability. For high-stakes decisions, route the validated output to a human review queue where a senior engineer can approve, reject, or annotate the analysis before it is stored as an Architecture Decision Record (ADR).

Avoid wiring this prompt into a continuous integration pipeline without human review. The model may miss undocumented organizational constraints, over-index on a single dimension like developer experience, or fail to account for upcoming platform changes. The harness should treat the model's output as a structured draft for human judgment, not a final decision. If the team profile indicates a strong existing bias (e.g., 'team only knows OpenAPI'), add a [CONSTRAINTS] instruction to explicitly challenge that default before invoking the prompt.