Guardrail Injection Without Task Breakage Prompt Template

This prompt is for AI product managers and safety engineers who need to embed content filters, bias mitigation rules, or refusal policies into a system prompt without breaking the assistant's core task. The job-to-be-done is not just adding a safety layer—it is adding one that is measurable, testable, and does not silently degrade output quality. The ideal user is someone who already has a working task prompt and a defined safety policy, and now needs to merge them without creating a prompt that over-refuses, under-performs, or collapses under adversarial pressure.

Use this prompt when you have a concrete primary task specification and a concrete guardrail policy, and you need placement rules that preserve both. Do not use this prompt if you are still defining what the guardrail should be, or if you are building a general-purpose chatbot with no measurable task success criteria. The prompt assumes you can define a task quality metric and a guardrail effectiveness metric, and that you are prepared to run A/B tests comparing the unguarded baseline, a naively guarded variant, and the placement-optimized variant. Required inputs include the base system prompt, the guardrail policy text, a task evaluation rubric, and a set of adversarial or edge-case inputs that probe the guardrail boundary.

This prompt is not a substitute for product-level safety architecture. If your use case involves regulated domains, high-stakes decisions, or user-generated content that could cause real-world harm, you must combine this prompt with human review, logging, and runtime guardrail tooling. The prompt helps you find the least-destructive injection point for a policy; it does not guarantee the policy itself is correct, complete, or legally sufficient. After using this prompt, your next step is to run the A/B test it designs and compare task quality scores against guardrail violation rates before shipping.

This template is designed to be placed within a system prompt to layer safety instructions alongside task instructions. Its core mechanism is an explicit priority stack that forces the model to resolve conflicts between the user's task and the safety policy before generating a response. The placeholders allow you to adapt it to your specific policy documents, task descriptions, and risk tolerance. The key structural choice is separating the 'policy check' from the 'task execution' to prevent the model from blending refusal language into a helpful answer or, conversely, ignoring a safety rule to complete a task.

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SYSTEM INSTRUCTION PRIORITY STACK:
1. [SAFETY_POLICY_DOCUMENT]: This is the highest priority. You must comply with all rules defined here.
2. [TASK_INSTRUCTION]: Execute this instruction only if it does not violate any rule in [SAFETY_POLICY_DOCUMENT].

EXECUTION PROTOCOL:
- Step 1: Analyze the [USER_INPUT] against every rule in [SAFETY_POLICY_DOCUMENT].
- Step 2: If a violation is detected, you must ONLY output the refusal response specified in [SAFETY_POLICY_DOCUMENT]. Do not execute [TASK_INSTRUCTION]. Do not explain the task.
- Step 3: If no violation is detected, execute [TASK_INSTRUCTION] on [USER_INPUT] completely and exclusively. Do not mention the safety check.
- Step 4: If [TASK_INSTRUCTION] requires a tool that is not in [AVAILABLE_TOOLS], or if the task is outside your capabilities as defined in [CAPABILITY_BOUNDARY], respond with the fallback message in [FALLBACK_POLICY].

CONSTRAINT: Never combine a refusal with a task output. The response must be either a full refusal or a full task execution.

[SAFETY_POLICY_DOCUMENT]:
[INSERT_YOUR_STRUCTURED_POLICY_HERE]

[TASK_INSTRUCTION]:
[INSERT_YOUR_PRIMARY_TASK_PROMPT_HERE]

[AVAILABLE_TOOLS]:
[INSERT_TOOL_DEFINITIONS_IF_ANY]

[CAPABILITY_BOUNDARY]:
[INSERT_CAPABILITY_DISCLOSURE]

[FALLBACK_POLICY]:
[INSERT_FALLBACK_MESSAGE]

To adapt this template, replace each bracketed section with your concrete policy and task definitions. The [SAFETY_POLICY_DOCUMENT] should be a structured, unambiguous list of rules, not aspirational prose. For example, instead of 'Be safe,' use 'Rule 1: If the user asks for instructions on illegal activities, refuse with message X.' The [TASK_INSTRUCTION] should be your complete, unmodified prompt for the primary job-to-be-done. After adaptation, you must run a battery of tests where a single input is designed to trigger both a policy violation and a valid task to ensure the priority stack holds and the output is not a broken hybrid of the two. A common failure mode is the model outputting a refusal followed by 'However, I can tell you...' which breaks the constraint. Your eval criteria must explicitly penalize this pattern.

The Guardrail Injection Without Task Breakage prompt is designed to be called programmatically as part of a policy authoring pipeline, not as a one-off chat interaction. The typical integration point is a CI/CD step that runs whenever a new safety policy or behavioral rule is proposed for a production assistant. The application should supply the primary task prompt, the candidate guardrail policy, and the task evaluation criteria as structured inputs. The prompt returns a placement recommendation, an injection strategy, and a predicted performance impact score that downstream systems can use to decide whether to deploy, revise, or reject the guardrail.

Implement this as a gated workflow with three stages. First, call the prompt with the proposed guardrail and a representative task evaluation set. Parse the JSON output and extract the placement_strategy, injection_location, and performance_impact fields. Second, run an automated A/B evaluation: execute the primary task prompt with and without the injected guardrail against a golden dataset of at least 50 task examples. Measure task quality using the evaluation criteria specified in the prompt's [EVAL_CRITERIA] input. Third, compare the measured performance delta against the predicted impact from the prompt output. If the actual degradation exceeds the predicted threshold or falls below a predefined acceptance boundary (e.g., task accuracy drops more than 3%), block deployment and route the guardrail for human revision. Log all results—including the prompt's raw output, the A/B scores, and the deployment decision—to an audit store for governance review.

For high-risk domains such as healthcare or finance, add a mandatory human approval gate after the automated A/B test, even if the performance delta is within acceptable limits. The human reviewer should inspect the guardrail placement, the injection wording, and a sample of task outputs from both arms of the test. Use a review checklist that includes: Does the guardrail fire on appropriate inputs? Does it suppress legitimate task responses? Does it introduce new failure modes? Only after sign-off should the guardrail be merged into the production system prompt. Avoid deploying guardrails directly from prompt output without this evaluation loop—raw placement recommendations can be syntactically correct but semantically destructive in ways that only task-level testing reveals.