A data agent designed to convert natural language questions into SQL queries needs an explicit "intent gate" to handle ambiguity before generating code. This gate should identify potential ambiguities in metrics, dimensions, timeframes, or scope, and prompt the user for clarification rather than making silent assumptions. Representing the query intent as a structured state allows for clear transitions from ambiguous to resolved intent, ensuring the generated SQL accurately reflects the user's business needs. AI
IMPACT Ensures Text-to-SQL agents produce more accurate and business-aligned results by clarifying user intent.
RANK_REASON Article describes a method for improving an existing type of AI tool (Text-to-SQL agents).
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