Enterprise Document Intelligence presents a practical solution for handling vague questions in RAG systems. Rather than repeatedly asking users for clarification, the approach uses a single targeted question to learn defaults that apply to future queries. The system emits a Clarification Request when parsed questions lack confidence, then stores learned defaults to avoid redundant prompts.

Two Pydantic schemas power this mechanism: Clarification Request captures missing fields and proposed defaults, while Clarification Default tracks user votes and confidence scores across interactions. The confidence threshold drives behavior: below 0.6 always prompts, above 0.85 applies silently, with a middle range for occasional refresh. This creates a production-ready feedback loop that improves over time.

A worked broker example demonstrates real-world application. When a junior adjuster asks about an insurer on a new contract, the system learns from the first interaction. By the twelfth similar case, it applies stratified defaults based on document characteristics like page types. The mechanism tracks explicit agreements, implicit acceptance through correct answers, and failure detection for continuous improvement.

This pattern addresses common production failures: ambiguous field types, missing page scope, and unclear entity references. Instead of guessing or refusing requests, systems can learn from minimal user interaction. The result is reduced friction for users while maintaining accuracy across diverse document types.