Boundary Condition Rejection Prompt Template

This prompt template is designed for QA engineers, prompt testers, and production engineers who need to harden an AI system against inputs that fall outside its operational envelope. The job-to-be-done is not general safety refusal, but strict, predictable rejection of inputs that violate explicit technical constraints: maximum token length, null or missing required fields, conflicting parameters, unsupported data types, or values outside an allowed range. The ideal user is someone integrating a model into a software pipeline where silent failures or undefined behavior at the boundaries are unacceptable. They need the model to act as a deterministic gate, returning a structured rejection object rather than attempting a best-effort guess.

This prompt is appropriate when you have a well-defined input schema and a finite list of boundary conditions that can be enumerated in a few-shot format. It works best when the rejection criteria are objective—a field is null, a string exceeds 256 characters, a date is in the past—rather than subjective or policy-based. You should use this prompt when downstream systems depend on clean, validated inputs and cannot tolerate hallucinated data or format drift. It is not a replacement for application-layer validation; the prompt should be the last line of defense, catching edge cases that are difficult to express in code alone, such as semantic conflicts between multiple fields. The prompt template includes slots for [INPUT_SCHEMA], [BOUNDARY_CONDITIONS], [POSITIVE_EXAMPLES], and [NEGATIVE_EXAMPLES], allowing you to teach the model the exact shape of acceptable and unacceptable inputs through demonstration.

Do not use this prompt when the boundary conditions are ambiguous, require external knowledge, or involve nuanced policy judgments. If you are trying to teach the model to refuse unsafe requests, use a Safety Policy and Refusal Prompt from the safety pillar instead. If you need to handle malformed outputs after generation, use an Output Repair and Validation Prompt. This prompt is also a poor fit for dynamic schemas that change frequently, as the few-shot examples will drift and require constant maintenance. Before deploying, pair this prompt with a validation harness that parses the structured rejection output and cross-checks it against your application-layer validation. For high-stakes pipelines, log every rejection for human review during the first week of production to catch false positives where the model rejects a valid but unusual input.

This template is designed to be dropped into a system prompt or a few-shot instruction block where the model must learn to refuse, escalate, or request clarification when an input violates a defined operational boundary. Unlike a simple instruction like 'reject invalid inputs,' this template uses explicit negative demonstrations to teach the model what rejection looks like for specific boundary conditions—max length, null fields, conflicting constraints, or out-of-domain requests. The square-bracket placeholders let you adapt the template to your exact schema, risk tolerance, and rejection style without rewriting the core logic.

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You are a boundary-aware input processor. Your job is to accept valid inputs that meet all constraints and reject inputs that violate operational boundaries.

[INPUT_SCHEMA]

[CONSTRAINTS]

[REJECTION_RULES]

Here are examples of correct behavior:

[POSITIVE_EXAMPLES]

Here are examples of boundary violations and the required rejection response:

[NEGATIVE_EXAMPLES]

Now process the following input. If it violates any constraint, output a rejection object. If it is valid, output the accepted object.

Input: [USER_INPUT]

Output:

Placeholder Definitions:

• [INPUT_SCHEMA]: The expected structure of a valid input. Use JSON Schema, a typed field list, or a natural-language description. Example: A JSON object with required fields 'user_id' (string, non-empty), 'query' (string, 1-500 characters), and 'filters' (optional array of strings).
• [CONSTRAINTS]: The operational boundaries that trigger rejection. Be explicit. Example: Reject if: query is empty, query exceeds 500 characters, user_id is null or missing, filters contains more than 10 items, or the input contains any field not defined in the schema.
• [REJECTION_RULES]: How the model should reject. Define the output shape for rejections. Example: When rejecting, output a JSON object with 'status': 'rejected', 'reason': a specific violation code from [VIOLATION_CODES], and 'detail': a one-sentence explanation of what boundary was violated.
• [POSITIVE_EXAMPLES]: 2-4 examples of valid inputs and their accepted outputs. These establish the baseline behavior.
• [NEGATIVE_EXAMPLES]: 3-6 examples of boundary violations paired with the exact rejection output you expect. Include near-miss cases (e.g., query at 501 characters, filters with 11 items) to teach edge discrimination. Each negative example should show the violating input and the correct rejection object.
• [USER_INPUT]: The runtime input to evaluate. In production, this is injected by your application harness.

Adaptation guidance: Start with 3-4 negative examples that cover your most common production boundary failures. If you observe the model accepting inputs that should be rejected, add a negative example that mirrors that exact failure pattern. If the model over-rejects, add a positive example that is close to the boundary but valid. For high-stakes domains, include a [RISK_LEVEL] field that adjusts rejection strictness—'low' might allow the model to request clarification for ambiguous cases, while 'high' requires immediate rejection with no further processing. Always pair this template with a validation harness that checks rejection accuracy, false positive rate, and false negative rate before deployment.

This prompt is not a standalone chat instruction; it is a programmable validation gate designed to sit between user input and a downstream AI workflow. The primary integration pattern is a pre-processing step: user input arrives, the Boundary Condition Rejection Prompt evaluates it against a defined operational contract, and the system either passes the input forward or returns a structured rejection. This prevents downstream models from wasting tokens on malformed requests, hallucinating on null fields, or executing actions on out-of-bounds parameters. The harness must enforce a strict contract: the prompt's output is a machine-readable decision, not a conversational reply.

Wire the prompt into your application as a synchronous validation service. The request payload should include the user input and a machine-readable [CONSTRAINTS] block (e.g., max length, required fields, allowed enum values). The model's response must be parsed as a JSON object with at minimum a decision field (pass or reject) and a reason field. Implement a post-processing validator that checks for schema conformance before trusting the decision. If the model returns malformed JSON, retry once with a stricter output instruction; if it fails again, default to a safe rejection and log the raw output for debugging. For high-throughput systems, consider using a smaller, faster model for this gate and reserving larger models for the primary task. Log every rejection with the input hash, constraint set, and reason to build an edge-case dataset for future prompt tuning.

The most common failure mode is over-rejection due to ambiguous constraints. If your [CONSTRAINTS] block says 'input must be a valid address' without defining 'valid,' the model will reject edge cases that a downstream system could handle. Mitigate this by pairing the prompt with a constraint validation test suite: a set of inputs that should pass and a set that should reject, run against every prompt change. Track rejection accuracy (precision and recall) as a key metric. For high-risk domains where a false pass could trigger a destructive action, add a human review step for any input that falls within a configurable 'uncertainty band'—for example, when the model's rejection reason contains hedging language like 'appears to be' or 'might be.' This harness turns a prompt into a reliable, auditable component of a production AI system.