System Prompt Rollback Trigger Definition Prompt

This prompt is for Site Reliability Engineering (SRE) and AI operations teams who need to define automated conditions that trigger a rollback of frozen system instructions. When a system prompt is frozen, any degradation in production behavior requires a fast, evidence-based decision to revert to a previous stable version. This prompt takes your observability signals, behavioral baselines, and risk tolerances as input and produces precise threshold definitions, monitoring query templates, and a reference to the rollback procedure.

Use this prompt after you have a frozen system prompt in production and a monitoring pipeline that captures behavioral metrics, error rates, or policy violation counts. Do not use this prompt during initial prompt development or before you have established a behavioral baseline. The output is not a rollback decision itself—it is the trigger definition that your monitoring system will evaluate. You must have at least 7 days of stable production data to establish meaningful thresholds. Without a baseline, the prompt will produce arbitrary numbers that generate either alert floods or silent failures.

Before running this prompt, gather your production metrics for the frozen prompt: request volume, error rate, policy violation count, latency distribution, and any custom behavioral scores your team tracks. Also prepare your organization's risk tolerance statement—for example, 'we accept up to a 2% increase in policy violations before triggering review' or 'any regression in refusal accuracy above 5% requires immediate rollback.' The prompt will reject vague tolerances like 'we want it to work well.' After generating trigger definitions, wire them into your alerting system with a 15-minute evaluation window to avoid flapping, and always include a human-in-the-loop confirmation step before automated rollback executes in production.

This prompt is designed for SRE and AI operations teams who need to move from reactive incident response to predefined, automated rollback conditions. It takes your observability data, behavioral baselines, and operational constraints as input and produces structured trigger definitions that can be directly translated into monitoring rules and runbook procedures. The output includes threshold definitions, specific monitoring queries, rollback procedure references, and an evaluation of each trigger's sensitivity, specificity, and alert fatigue risk.

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You are an SRE engineer specializing in AI system reliability. Your task is to define automated rollback triggers for frozen system prompts based on production observability data.

## INPUTS
- Current frozen system prompt version: [FROZEN_PROMPT_VERSION]
- Behavioral baseline snapshot: [BASELINE_SNAPSHOT]
- Production monitoring data (last [TIME_WINDOW]): [MONITORING_DATA]
- Acceptable behavioral drift thresholds: [DRIFT_THRESHOLDS]
- Known failure modes from pre-freeze testing: [KNOWN_FAILURE_MODES]
- Rollback procedure reference: [ROLLBACK_RUNBOOK_URL]
- Alerting system constraints: [ALERTING_SYSTEM_CONSTRAINTS]
- Stakeholder severity definitions: [SEVERITY_DEFINITIONS]

## OUTPUT SCHEMA
Return a JSON object with the following structure:
{
  "trigger_definitions": [
    {
      "trigger_id": "string",
      "trigger_name": "string",
      "condition": "string (precise, queryable condition)",
      "monitoring_query": "string (the exact query to evaluate the condition)",
      "severity": "critical|high|medium|low",
      "threshold_value": "number",
      "evaluation_window": "string (e.g., '5m', '1h', '24h')",
      "rollback_action": "string (reference to rollback procedure step)",
      "sensitivity_assessment": {
        "expected_true_positive_rate": "string",
        "expected_false_positive_rate": "string",
        "alert_fatigue_risk": "high|medium|low",
        "rationale": "string"
      },
      "cooldown_period": "string",
      "requires_human_approval": true|false
    }
  ],
  "trigger_dependencies": [
    {
      "trigger_id": "string",
      "depends_on": ["trigger_id"],
      "relationship": "string"
    }
  ],
  "coverage_gaps": [
    {
      "failure_mode": "string",
      "gap_description": "string",
      "recommended_mitigation": "string"
    }
  ],
  "implementation_notes": "string"
}

## CONSTRAINTS
- Every trigger condition must be directly queryable from [MONITORING_DATA] without manual interpretation.
- Threshold values must be justified by the behavioral baseline, not arbitrary.
- For each trigger, explicitly assess the trade-off between detection speed and false positive rate.
- If a known failure mode from [KNOWN_FAILURE_MODES] has no corresponding trigger, list it in coverage_gaps with a recommended mitigation.
- Triggers with severity "critical" must require human approval before automated rollback executes.
- Cooldown periods must prevent trigger storms during transient degradation.
- Do not propose triggers that depend on data not present in [MONITORING_DATA].

## RISK LEVEL
[RISK_LEVEL]

Adaptation guidance: Replace each square-bracket placeholder with your production data. The [BASELINE_SNAPSHOT] should be the output of a Production Behavior Baseline Capture Prompt run before the freeze. The [MONITORING_DATA] should include latency distributions, error rates, refusal rates, output schema compliance scores, and behavioral drift metrics. If your observability stack cannot provide a specific metric, remove the corresponding trigger condition rather than inventing a proxy. For high-risk deployments, add a requires_human_approval: true constraint to all triggers above medium severity and route approvals through your incident management system.

What to do next: After generating trigger definitions, validate each monitoring query against your actual observability backend before deployment. Run a silent mode test for at least one full evaluation window to measure the true false positive rate against production traffic. If any trigger fires during silent mode without a corresponding real degradation event, adjust the threshold or widen the evaluation window before enabling automated rollback. Document the final trigger set in your runbook and ensure the rollback procedure referenced in [ROLLBACK_RUNBOOK_URL] is tested and current.

This prompt is designed to be called by an automated reliability pipeline, not a human chat interface. The primary integration point is a monitoring system that detects behavioral drift or policy violations in frozen system prompts. When a potential degradation is flagged, the pipeline should assemble the required inputs—production behavior samples, the frozen baseline, current monitoring metrics, and the organization's rollback policy—and invoke this prompt to produce a structured trigger definition. The output is not a rollback decision itself but a machine-readable specification that a subsequent automation step can evaluate against live data streams.

Wire the prompt into a workflow engine (e.g., Temporal, Airflow, or a CI/CD pipeline with a manual approval gate) with the following stages: Input Assembly—collect the [FROZEN_PROMPT_VERSION], [PRODUCTION_BEHAVIOR_SAMPLES], [BASELINE_REFERENCE], [MONITORING_METRICS], and [ROLLBACK_POLICY_DOC] from your observability stack and version control. Prompt Execution—call the model with temperature=0 and response_format set to the defined JSON schema to ensure deterministic, parseable output. Output Validation—validate the returned JSON against the [OUTPUT_SCHEMA] using a schema validator (e.g., jsonschema in Python). Reject and retry once if validation fails; escalate to human review after a second failure. Threshold Evaluation—feed the trigger_conditions and threshold_definitions into your metrics engine (e.g., Prometheus alerting rules or a custom evaluator) to determine if any trigger has fired. Rollback Decision—if a trigger fires, route to the rollback procedure referenced in the output's rollback_procedure_reference field, which should include a runbook link and required approvers. Log every invocation, including the prompt version, input hashes, model response, validation result, and trigger evaluation outcome, to an audit store for post-incident review.

The highest-risk failure mode in this harness is alert fatigue from overly sensitive triggers. To mitigate this, implement a dampening layer: require that a trigger condition evaluates to true for at least two consecutive evaluation windows before escalating. Additionally, run a weekly silent evaluation of the trigger definitions against historical data to measure the false-positive rate. If the rate exceeds the threshold defined in your rollback policy, flag the prompt's output for human review and recalibration. Do not allow automated rollback without a human approval gate for any system prompt serving production traffic above a [RISK_LEVEL] of 'high'. For lower-risk environments, automated rollback may be acceptable, but always require a post-rollback analysis ticket to be created automatically. The model choice should prioritize reliability and schema adherence over creativity; a model with strong JSON mode support is essential. Avoid using this prompt in isolation—it must be part of a broader instruction freeze governance pipeline that includes pre-freeze testing, baseline capture, and post-freeze monitoring.