Spurious Field Removal Prompt for JSON Outputs

This prompt is designed for API developers and integration engineers who need to sanitize model-generated JSON payloads before they reach downstream systems. The core job-to-be-done is identifying and stripping any field—at any nesting level—that was invented by the model and does not belong to a predefined, expected schema. This is a post-generation repair step, not a schema enforcement tool for the initial generation. Use it when you have a valid JSON output that structurally parses but contains extra, hallucinated keys that would cause database constraint violations, API contract mismatches, or data pipeline errors.

The ideal user has a strict output contract—an OpenAPI schema, a database model, or a typed data class—and needs to guarantee that only known fields pass through. You must provide the prompt with both the generated JSON and the expected schema definition. The prompt works best when the schema is expressed as a list of valid top-level and nested field paths, not just a JSON Schema document, because it needs to reason about field provenance rather than type validity. Do not use this prompt when the model output is already structurally broken (e.g., unparseable JSON); that requires a malformed JSON repair prompt first. Do not use it when you need to coerce types or normalize values—this prompt only removes spurious fields, it does not fix the values of valid fields.

Before implementing this prompt in production, define your tolerance for false positives. A field that is valid but unexpected in a specific context might be stripped incorrectly. Mitigate this by logging every removal with the field path and reason, and consider routing removals to a human review queue if the downstream system is in a regulated domain such as finance or healthcare. Pair this prompt with a schema validation step after stripping to confirm the output is now contract-compliant. If the model consistently hallucinates the same spurious fields, investigate your generation prompt and context rather than relying solely on this repair loop.

This prompt template is designed to be copied directly into your application or evaluation harness. It instructs the model to act as a strict JSON validator, comparing an input payload against a provided schema and removing any field—at any nesting level—that is not explicitly defined. The template uses square-bracket placeholders for all dynamic inputs, making it safe to inject variables programmatically without confusing the model's own output format.

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You are a strict JSON schema validator. Your task is to clean a JSON payload by removing any field that is not defined in the expected schema.

## INPUT
[JSON_PAYLOAD]

## EXPECTED SCHEMA
[SCHEMA_DEFINITION]

## INSTRUCTIONS
1. Parse the INPUT as a JSON object.
2. Compare every key at every nesting level against the EXPECTED SCHEMA.
3. If a key exists in the INPUT but is NOT defined in the EXPECTED SCHEMA, remove that key-value pair entirely.
4. Preserve the structure, order, and values of all valid fields.
5. If a valid field contains a nested object, recursively validate that nested object against the corresponding schema definition.
6. If a valid field contains an array of objects, validate each object in the array against the schema definition for that array's items.
7. Do not add, modify, or re-type any valid values.
8. Do not alter the schema-defined structure.

## CONSTRAINTS
- [ADDITIONAL_CONSTRAINTS]

## OUTPUT
Return ONLY the cleaned JSON object. Do not include explanations, markdown fences, or commentary.

To adapt this template, replace [JSON_PAYLOAD] with the model's raw output, [SCHEMA_DEFINITION] with your target schema expressed as a JSON Schema, TypeScript interface, or a clear natural-language description of allowed fields, and [ADDITIONAL_CONSTRAINTS] with any domain-specific rules such as preserving null values for optional fields or logging removed keys. For high-risk workflows where a spurious field could cause downstream data corruption, always pair this prompt with a post-execution validation step that programmatically confirms the cleaned output conforms to the schema before it reaches any database or API.

Integrating the Spurious Field Removal prompt into an application requires treating it as a post-generation repair step within a structured output pipeline. The typical flow is: (1) the primary model generates a JSON payload, (2) a lightweight schema validator checks the payload against the expected schema, (3) if spurious fields are detected, the payload and schema are passed to this prompt, and (4) the cleaned payload is re-validated before proceeding downstream. This prompt should not be called on every response—only when validation fails with 'additionalProperties' or unknown field errors—to avoid unnecessary latency and cost.

The implementation harness must include a validation gate before and after the prompt. Before calling the prompt, confirm that the failure is specifically due to extra fields, not missing required fields or type mismatches. After receiving the cleaned output, run the same schema validator again. If spurious fields persist, implement a retry loop with a maximum of 2 attempts, appending the validator's specific error messages to the [CONSTRAINTS] block on retry. If the output still fails after retries, log the full payload, schema, and prompt trace for manual review rather than silently dropping fields. For nested objects and arrays, ensure your validator recursively checks all levels—many validators only check the top-level keys by default.

Model choice matters for this workflow. The prompt relies on strict instruction-following and structural reasoning, so prefer models with strong JSON manipulation capabilities (e.g., GPT-4o, Claude 3.5 Sonnet, or fine-tuned variants). Avoid using this prompt with models under 7B parameters unless you've validated their ability to recursively traverse nested schemas. For high-throughput systems, consider implementing a deterministic pre-filter that strips fields not in the schema before calling the LLM—this reduces prompt calls for simple cases. Always log every removal decision with the field path, removed value, and reason code for auditability. In regulated environments, route outputs with removed fields to a human review queue before they enter databases or user-facing surfaces.