Question & Answer Pairs

Conversations often contain questions, and sometimes the answers appear later in the same discussion. By extracting Q&A pairs, teams quickly see which inquiries were resolved and which remain open. This visibility improves follow-up efficiency, helps CX teams address customer concerns, and informs strategic decisions.

Who Benefits:

• Project Managers & Team Leads: Instantly know which questions got answered, ensuring everyone leaves the meeting informed.
• CX Teams: Confirm that customer questions were addressed promptly or note where follow-ups are needed.
• Business Analysts & Strategists: Identify common inquiries and how they’re resolved, guiding product or process improvements.
• Compliance & QA Officers: Verify that required inquiries received appropriate responses, ensuring regulatory or policy adherence.

Value Proposition:
Structured Q&A pairs enable clear, fast insights into conversation effectiveness, ensuring no critical question goes unanswered without awareness.

Data Dictionary & Schema

Objective:
Extract Q&A pairs from the transcript. Each pair includes one question and its corresponding answer (if found). If no answer exists, mark answer as null.

Schema:

{
  "qas": [
    {
      "id": "<string>",
      "question": "<string>",
      "answer": "<string or null>",
      "type": "qa",
      "score": <number>,
      "entities": [
        {
          "type": "<string>",
          "text": "<string>",
          "value": {
            "channel": "<string>"
          }
        }
      ]
    }
  ]
}

Data Dictionary (Simplified):

If No Q&A Pairs:

{
  "qas": []
}

Confidence & Calibration

Confidence Guidelines:

• High (0.8–1.0): Direct, clear answer to the identified question.
• Medium (0.5–0.8): The answer partially or indirectly addresses the question.
• Low (<0.5): Uncertain if the selected answer truly responds to the question. Consider omitting or using a low score.

Calibration Steps:

1. Test on Various Scenarios: Check transcripts with straightforward and ambiguous Q&As.
2. Refine Entity Detection: If difficulty arises in identifying who is involved, add domain-specific hints.
3. Stakeholder Feedback: Adjust thresholds or clarity based on input from PMs, CX teams, or compliance officers.
4. Iterative Improvement: Update instructions as language patterns evolve.

This approach ensures evolving accuracy and relevance.

Prompt Construction & Instructions

Role Specification & Reiteration:

• The system is a “highly experienced assistant” specializing in extracting Q&A pairs.
• Reiterate key instructions to ensure no confusion.
• No reasoning text, no extra commentary—only the final JSON.

Avoid Hallucination:

• Extract only Q&A pairs genuinely found in the transcript.
• If uncertain about the correctness of the answer, assign a lower score or omit that Q&A pair.
• If no answer is found for a question, set answer to null.

Strict Formatting:

• Return only the JSON.
• If no Q&A pairs, return "qas": []".

Prompt for Implementation

System Message (Role: System):
"You are a highly experienced assistant that extracts question-and-answer (Q&A) pairs from a timestamped, diarized conversation transcript. Each Q&A pair consists of one question and its corresponding answer (if present). Your output must adhere to this JSON schema:

{
  "qas": [
    {
      "id": "<string>",
      "question": "<string>",
      "answer": "<string or null>",
      "type": "qa",
      "score": <number>,
      "entities": [
        {
          "type": "<string>",
          "text": "<string>",
          "value": {
            "channel": "<string>"
          }
        }
      ]
    }
  ]
}

Instructions (Reiterated):

1. Identify questions and their corresponding answers from the transcript.
2. Assign a unique id (e.g. "qa-1") to each Q&A pair.
3. question is the full question text.
4. answer is the answer text if available, otherwise null.
5. type must be "qa".
6. score indicates confidence in the Q&A match.
7. If a channel (e.g., a person/team) is identified, represent it in entities.
8. Return only this JSON—no extra text or reasoning.
9. If no Q&A pairs are found, return "qas": []".

Chain-of-Thought (Hidden):

• Reason silently, do not display reasoning steps.

No Hallucination:

• Only produce Q&A pairs actually present in the transcript.
• If uncertain about correctness, lower the score or omit the pair.

System Summary:
Read the transcript, identify Q&A pairs, and return them strictly in the given JSON format. Nothing else."

User Message (Role: User):
"Analyze the following conversation and return the Q&A pairs as instructed:

[TRANSCRIPT_JSON][TRANSCRIPT_JSON]"

(Replace [TRANSCRIPT_JSON] with your actual conversation data.)