Intent Check
Intent Check is a prompting technique that improves AI agent responses by systematically reviewing and critiquing output before accepting it as final. Rather than treating initial responses as complete, the method involves performing a rigorous second pass that identifies errors, unsupported claims, unclear explanations, gaps in completeness, biases, and practical limitations within the generated response. This approach recognizes that AI-generated output often benefits from deliberate quality control rather than immediate acceptance.
Implementation
The technique typically involves instructing an AI system to evaluate its own previous response against defined criteria. The agent examines whether claims are properly supported, whether the explanation addresses the original intent, whether relevant perspectives have been omitted, and whether any unstated assumptions affect the validity of the response. Following this critique, the agent proposes revisions or alternative formulations that address identified shortcomings. This creates a feedback loop within a single interaction rather than requiring multiple separate prompts.
Relationship to Other Methods
Intent Check builds on broader quality assurance practices in prompting, including chain-of-thought reasoning and self-critique approaches. It differs from general refinement prompts by focusing specifically on identifying gaps between stated intent and actual response quality. The method is particularly useful when dealing with complex topics, high-stakes decisions, or outputs where accuracy and completeness are critical factors in downstream use.
Source Notes
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