Boolean Argument Inference from Affirmative Language Prompt

This prompt is for AI engineers and platform developers who need to map natural language affirmations, negations, and hedges into reliable boolean (true/false) values for tool call arguments. The job-to-be-done is converting user statements like 'yeah go ahead,' 'no don't do that,' or 'I think so' into a typed boolean that a downstream function can consume without crashing. This is not a general sentiment classifier; it is a focused inference step inside a tool-calling pipeline where a specific boolean parameter must be populated from conversational input.

Use this prompt when the user's intent is expressed through affirmative or negative language rather than an explicit toggle. Common scenarios include confirmation gates ('Should I proceed?'), feature flags ('Turn on notifications'), and permission checks ('Are you sure?'). The prompt requires a clearly defined boolean parameter name, the user's conversational text, and optional context from prior turns. It should not be used when the user provides an explicit true/false literal, when the decision requires multi-step reasoning over structured data, or when the boolean value depends on business logic that the model cannot access. In regulated domains, the inferred boolean must be paired with a confidence score and source attribution before execution.

Before wiring this into production, define your ambiguity threshold. Hedged language like 'probably,' 'I guess so,' or 'maybe not' should trigger a low-confidence flag rather than a forced boolean. The next step is to combine this prompt with a validation harness that checks the output against a strict {value: boolean, confidence: number, source: string} schema. If confidence falls below your threshold, route to a clarification prompt or escalate for human review. Never treat a model-inferred boolean as equivalent to a user-selected checkbox without an audit trail.

This prompt template is designed to be inserted into a tool-calling pipeline where a specific boolean parameter must be resolved from conversational language. It instructs the model to analyze the user's input for explicit affirmations ("yes," "enable"), explicit negations ("no," "skip"), hedged signals ("maybe," "if"), and implicit defaults. The output is a structured JSON object containing the inferred boolean value, a confidence score, and the evidence span, rather than a raw true or false. This structure is critical for downstream gating, auditability, and deciding when to escalate to a human for clarification.

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You are an argument-resolution specialist. Your task is to infer a boolean value for the parameter [PARAMETER_NAME] from the user's input. Do not guess. Use only the evidence provided.

**Parameter Context:**
[PARAMETER_DESCRIPTION]

**User Input:**
"""
[USER_INPUT]
"""

**Instructions:**
1.  **Analyze the user's language** for the following signals regarding [PARAMETER_NAME]:
    *   **Explicit Affirmation:** Direct "yes", "enable", "turn on", "set to true".
    *   **Explicit Negation:** Direct "no", "disable", "turn off", "skip", "set to false".
    *   **Hedged or Conditional Language:** "maybe", "only if", "I guess", "not sure".
    *   **Implicit Signal:** The user's intent is clear from context without a direct keyword (e.g., "Go ahead and process it" implies affirmation for a `should_process` flag).
    *   **Absent Signal:** The user's input contains no information about this parameter.

2.  **Determine the output based on the strongest signal:**
    *   If an explicit signal is found, set the `inferred_value` to the corresponding boolean and set `confidence` to `high`.
    *   If an implicit signal is found, set the `inferred_value` and set `confidence` to `medium`.
    *   If a hedged signal is found, set the `inferred_value` to the most likely boolean but set `confidence` to `low`.
    *   If the signal is absent or completely ambiguous, set `inferred_value` to `null` and `confidence` to `none`.

3.  **Output a single JSON object** conforming to the schema below. Do not include any other text.

**Output Schema:**
{
  "parameter_name": "[PARAMETER_NAME]",
  "inferred_value": true | false | null,
  "confidence": "high" | "medium" | "low" | "none",
  "evidence_span": "The exact substring from the user input used to make the inference, or null.",
  "reasoning": "A concise, one-sentence explanation of the signal detected."
}

To adapt this template, replace the square-bracket placeholders with your specific context. [PARAMETER_NAME] should be the exact name of the boolean field in your tool's schema (e.g., send_notification). [PARAMETER_DESCRIPTION] must provide a clear, one-sentence definition of what the flag controls, as this grounds the model's understanding of implicit signals. [USER_INPUT] is the raw text from the user turn. For production use, the confidence field is the most critical output; you should implement a routing rule in your application harness that proceeds automatically only on high confidence, asks for confirmation on medium, and escalates to a clarification prompt on low or none. This prevents silent misconfiguration of boolean flags, which is a common source of logic errors in tool execution.

The Boolean Argument Inference prompt is not a standalone chatbot feature; it is a pre-execution argument resolution step inside a tool-calling pipeline. Its job is to consume a user utterance and a target boolean parameter definition, then return a structured decision: true, false, or a request for clarification. The calling application should treat this prompt as a deterministic function with a well-defined input contract and a strictly validated output schema. Wire it immediately before the tool dispatch step, after the tool has been selected but before its arguments are finalized. This placement ensures that boolean flags are resolved from natural language before the tool call is constructed, preventing malformed payloads from reaching downstream APIs.

Integration pattern: The prompt expects three inputs: [USER_UTTERANCE] (the raw user text), [PARAMETER_NAME] (the boolean flag being resolved), and [PARAMETER_CONTEXT] (a short description of what the flag controls, plus any domain-specific defaults). The output must conform to a strict JSON schema: {"value": boolean | null, "confidence": 0.0-1.0, "rationale": string, "needs_clarification": boolean, "clarification_question": string | null}. After receiving the model response, the application must validate that needs_clarification is true if and only if value is null, and that confidence is a float between 0 and 1. Reject any response that violates this invariant and retry once with an error message injected into the prompt context. If the retry also fails, escalate to a human review queue rather than guessing.

Confidence gating and fallback: Use the confidence score to decide whether to proceed automatically. For low-risk toggles (e.g., include_timestamps), a confidence threshold of 0.7 may be acceptable. For high-risk flags (e.g., delete_source_files, share_with_external), require a confidence of 0.95 or higher, and route anything below that threshold to a human approval step with the rationale and clarification_question displayed. Log every inference decision—including the raw user utterance, the resolved value, the confidence score, and whether it was auto-applied or escalated—to your observability platform. This audit trail is essential for debugging false positives (affirmative language misinterpreted as negation) and false negatives (hedged agreement treated as refusal).

Model choice and latency: This prompt is a single-turn classification task with a small output payload. Use a fast, cost-efficient model (e.g., GPT-4o-mini, Claude Haiku, or a fine-tuned small open-weight model) rather than a large reasoning model. The prompt should complete in under 500ms at P95 to avoid adding noticeable latency to the tool-calling flow. If you are resolving multiple boolean flags in a single user turn, batch them into one prompt call with an array of parameter definitions rather than making sequential calls. The output schema should expand to an array of per-parameter decisions, and the validation logic should iterate over each entry. Avoid the temptation to fold boolean inference into a larger argument-filling prompt unless you have evals that prove the combined prompt does not degrade boolean accuracy; boolean inference from hedged language is surprisingly sensitive to prompt context and benefits from focused, single-responsibility instructions.