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Data Extraction and Structured Output


Travel Booking Information Extraction
Challenge: Extract structured information from conversational data while handling missing information appropriately and avoiding hallucinations. Scenario: Processing travel booking conversations to extract key booking details in a structured format. Performance Comparison: Chat Model We evaluate the performance of a state-of-the-art chat model in two configurations:
  • Chat model alone (baseline)
  • Chat model enhanced with AI21 Maestro

Baseline Chat Model Output (Without AI21 Maestro)

Despite receiving explicit instructions, the chat model fails to properly handle partial information and makes unauthorized inferences: Input Prompt
Chat Model Output

Issues Identified

  • Instruction Violation: Added year “2023” despite explicit instruction not to infer
  • Compliance Failure: Ignored “fill with NA” requirement for incomplete dates

AI21 Maestro Performance

Input Prompt
Requirements
  1. If any field is not mentioned or can’t be fully extracted from the conversation, fill it with “NA”
  2. Do not invent, infer, or assume any details that are not explicitly stated in the conversation.
  3. return a bulleted list where each field is a bullet
Output