CI/CD Pipeline Prompt Gate Configuration Prompt Template

Platform engineers and MLOps teams often inherit release policies written in plain English—documents that describe what 'good enough' means for a prompt before it reaches production. These policies capture tribal knowledge about acceptable hallucination rates, required eval suites, and promotion rules across staging, canary, and production environments. The gap between that policy document and an executable gate configuration that a CI/CD system can enforce is where this prompt operates. It takes a human-readable policy and produces a structured gate specification that includes eval suite references, threshold values, required checks, timeout policies, and environment promotion rules.

Use this prompt when you are defining a new release gate for a prompt pipeline, standardizing promotion rules across multiple environments, or converting informal quality expectations into code that a CI/CD runner can evaluate automatically. The prompt assumes you already have eval suites defined and registered in your pipeline—it does not generate the evals themselves. It also assumes your CI/CD system can consume a structured configuration file and execute the specified checks. If you are still building your eval suites or designing your CI/CD integration from scratch, start with the Prompt Smoke Test Suite Generator or Eval Harness and Metric Selection playbooks first.

Do not use this prompt when the release policy is still in flux and undergoing active debate among stakeholders. The prompt produces a concrete, versioned artifact; generating it prematurely creates configuration drift and false confidence. Similarly, avoid this prompt for one-off manual reviews or ad-hoc quality checks—it is designed for automated pipeline enforcement, not human spot-checking. For high-risk domains where prompt failures carry regulatory or safety consequences, always pair the generated gate configuration with human approval gates and evidence collection steps. The Deployment Gate Evidence Collection and Human Approval and Escalation Prompts playbooks cover those requirements. Once you have a gate configuration, wire it into your CI/CD pipeline using the implementation patterns in the harness section, and validate it against historical eval runs before trusting it to block or promote real deployments.

This prompt template converts a human-readable release policy into a machine-executable gate configuration. It takes your policy statement, environment topology, and available eval suites as input, then outputs a complete gate spec with threshold values, required checks, timeout policies, and promotion rules. The output is designed to be consumed by a CI/CD orchestrator or a custom gate evaluation service, not just read by a human.

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You are a prompt release gate configuration generator. Your job is to convert a natural-language release policy into a structured, executable gate configuration for a CI/CD pipeline that deploys AI prompts.

## INPUTS

**Release Policy:**
[RELEASE_POLICY]

**Environment Topology:**
[ENVIRONMENT_TOPOLOGY]

**Available Eval Suites:**
[AVAILABLE_EVAL_SUITES]

**Historical Baseline Metrics (optional):**
[HISTORICAL_BASELINE_METRICS]

**Risk Tolerance:** [RISK_TOLERANCE]

## OUTPUT SCHEMA

Return a JSON object with this structure:

{
  "gate_name": "string",
  "gate_version": "string",
  "environments": [
    {
      "name": "string",
      "promotion_criteria": {
        "required_checks": [
          {
            "check_id": "string",
            "eval_suite": "string",
            "metric": "string",
            "operator": "gte" | "lte" | "eq" | "within_range",
            "threshold": number | [number, number],
            "sample_size_minimum": number,
            "blocking": boolean
          }
        ],
        "all_blocking_must_pass": boolean,
        "minimum_non_blocking_pass_rate": number
      },
      "timeout_seconds": number,
      "on_timeout": "fail" | "promote_with_warning" | "require_manual_review",
      "rollback_trigger": {
        "error_rate_threshold": number,
        "latency_p99_threshold_ms": number,
        "semantic_drift_threshold": number,
        "consecutive_failure_window": number
      }
    }
  ],
  "canary_config": {
    "enabled": boolean,
    "traffic_percentages": [number],
    "bake_time_seconds": number,
    "comparison_metric": "string",
    "degradation_tolerance": number
  },
  "global_timeout_seconds": number,
  "approval_requirements": {
    "auto_promote_if_all_pass": boolean,
    "require_manual_approval_for": ["string"],
    "bypass_justification_required": boolean
  },
  "evidence_collection": {
    "store_eval_outputs": boolean,
    "store_canary_comparisons": boolean,
    "retention_days": number
  }
}

## CONSTRAINTS

- Every check must reference an eval suite from the provided list.
- Threshold values must be justified by the release policy, not invented.
- If historical baseline metrics are provided, use them to set realistic thresholds. If not provided, mark thresholds as "requires_calibration" and explain why in a `calibration_notes` field.
- For high-risk environments, require manual approval regardless of automated results.
- Timeout values must account for eval suite execution time plus buffer.
- If the release policy is ambiguous about a threshold, choose the stricter interpretation and note the ambiguity in a `policy_interpretation_notes` field.
- Do not invent eval suites. Only reference suites from the provided list.

## OUTPUT FORMAT

Return ONLY the JSON object. No markdown fences, no explanatory text outside the JSON.

Adaptation guidance: Replace each square-bracket placeholder with concrete values from your pipeline context. [RELEASE_POLICY] should contain the full natural-language policy statement from your release manager or QA lead. [ENVIRONMENT_TOPOLOGY] should describe your deployment stages, such as staging -> canary-5% -> canary-25% -> production. [AVAILABLE_EVAL_SUITES] must list the exact eval suite names and their metrics that your CI/CD system can execute. If you don't have historical baselines, omit that field and the model will flag thresholds for calibration. The output JSON can be fed directly into a gate evaluation service or used to generate pipeline configuration files. Always validate the output against your actual eval infrastructure before deploying the gate—mismatched suite names or unsupported operators will cause pipeline failures at runtime.

This prompt is designed to be called programmatically within a CI/CD pipeline stage, not used interactively. It consumes a natural-language release policy and emits a machine-readable gate configuration. The calling application should treat the LLM output as a candidate configuration that must pass structural validation before being applied. The prompt is a transformation step: policy text in, structured gate config out. It does not execute the gates itself.

Wire the prompt into a pipeline job that runs after a prompt version is staged but before any canary or production deployment. The job should: (1) load the release policy from a version-controlled file or pipeline variable; (2) inject it into the [RELEASE_POLICY] placeholder; (3) call the model with response_format set to JSON and the expected schema; (4) validate the output against the gate configuration schema—check that every eval_suite references a known suite ID, every threshold is a number between 0 and 1, and every environment maps to a real deployment target; (5) on validation failure, retry once with the validation errors appended to the [CONSTRAINTS] field; (6) on success, write the configuration to an artifact store and pass it to the downstream gate execution stage. Use a model with strong JSON mode support and low temperature (0.0–0.1) for deterministic output.

For high-risk deployments, insert a manual approval step between configuration generation and gate execution. The approval UI should display the generated configuration alongside the source policy so reviewers can spot mismatches. Log every generated configuration, validation result, and approval decision for audit. If the prompt produces a configuration that fails validation after two retries, fail the pipeline stage and alert the platform team—do not silently fall back to a default gate config. The generated configuration should never be applied without passing schema validation and, where required, human review.