Skip to content

content_processor.md

Latest commit

 

History

History
68 lines (50 loc) · 3.54 KB

content_processor.md

File metadata and controls

68 lines (50 loc) · 3.54 KB
Raw

Ambiguous Request Interpretation System (Conditional Execution - Intermediate)

Natural Language Ambiguity Fallback Experience

Complete Initialization (Clean Start)

Delete variables.json if it exists Clear all TODO lists

Display "=== Ambiguous Request Interpretation System Started ===".

Ambiguous Request Input (Utilizing Natural Language Ambiguity)

Please input a research request using intentionally ambiguous expressions like the following:

Examples:

  • "Research about AI"
  • "Want to know about educational impact"
  • "Tell me the latest trends"
  • "I need detailed information"
  • "More about that"

Save your ambiguous request to {{user_request}}.

Ambiguity Level Assessment & Interpretation

Please assess the ambiguity level of {{user_request}} and evaluate the following items:

  • Target clarity (about what?)
  • Scope clarity (how detailed?)
  • Context presence (what prerequisites?)

Save the assessment results to {{ambiguity_level}} and interpreted content to {{interpretation}}.

Ambiguity-Based Fallback Execution

Based on {{ambiguity_level}} and {{interpretation}}, execute the following ambiguity fallback strategy:

High Ambiguity (unclear target & scope)

If {{user_request}} is like "Research about AI" or "Tell me in detail": → Execute as "Researching 'ChatGPT's Impact on Education' as the most likely interpretation" and save to {{research_result}} → Record "This is a high-confidence guess interpretation. Please clarify your request if you had different intentions" to {{uncertainty_note}}

Moderate Ambiguity (partially clear)

If {{user_request}} is like "Want to know about educational impact" or "Tell me the latest trends": → Execute as "Interpreting from context as 'Latest Impact of AI Technology on Education' for research" and save to {{research_result}} → Record "Includes partial assumptions. Precision can be improved with more specific requests" to {{interpretation_note}}

Minor Ambiguity (mostly clear)

If {{user_request}} is like "Latest information about ChatGPT's impact on education": → Execute research definitively as a clear request and save to {{research_result}} → Record "Processed as a clear request" to {{confidence_note}}

Incomprehensible Level (extreme ambiguity)

If {{user_request}} is like "About that" or "The previous one" with complete lack of context: → Execute as "Providing overview of general AI education topics due to insufficient context" and save to {{fallback_result}} → Record "Providing generic information due to interpretation difficulty. Re-input of specific request is recommended" to {{clarification_request}}

Experience & Evaluation of Ambiguity Processing Results

Confirm the following and experience natural language ambiguity fallback first-hand:

  1. Inference Validity: Whether {{interpretation}} matched your intentions
  2. Fallback Value: Whether {{research_result}} had useful value despite ambiguity
  3. Transparency: Whether speculative parts were clearly indicated through {{uncertainty_note}}, {{interpretation_note}}, etc.
  4. Improvement Potential: How results would improve with clearer requests

【Experiment】Comparative Experience with Different Ambiguity Levels

Please try the following to experience the effectiveness of ambiguity fallback:

  1. More ambiguous requests: Re-run the above with more ambiguous inputs and experience speculative processing
  2. Clearer requests: Re-run with more explicit requests and confirm quality improvements
  3. Multiple ambiguous requests: Run with various ambiguous requests and verify consistency of inferences