Documentation Index
Fetch the complete documentation index at: https://docs.inya.ai/llms.txt
Use this file to discover all available pages before exploring further.
Given below is the structure of the disposition prompt. Follow the structure to craft your own call-transcript extraction prompts:
1. Define the Role and Context
- What does it do?
Specify who the LLM is and the domain it operates in (e.g., Call Center Operations Analyst in Debt Collection).
- Why it matters:
Sets expectations and tailors the model’s responses.
2. Clarify the Task
- What does it do?
Write a brief "## Task" section that explains exactly what the model should accomplish (e.g., read transcript, extract fields).
- Why it matters:
Keeps the model focused on the end goal.
3. Emphasize Output Requirements
- What does it do?
Under "## IMPORTANT", stress the format (JSON) and forbid extra fields or custom codes.
- Why it matters:
Ensures structured, machine-readable output.
4. Provide Business and Language Context
- What does it do?
Use "## Context Understanding", "## Business Context" and "## Language Processing Guidelines" sections to share domain rules, use cases, and allowed languages.
- Why it matters:
Guides the model on tone, terminology, and multilingual handling.
5. List Field Definitions
- What does it do?
Create a JSON snippet showing each key with an empty value.
- Why it matters:
Shows exactly which fields to populate and in what structure.
6. Detail Allowed Values
- What does it do?
Under "### Allowed Values & Definitions", enumerate each field’s valid codes, descriptions, and criteria in priority order for STAGE_CODE.
- Why it matters:
Prevents misclassification and enforces consistency.
7. Add Critical Analysis Instructions
- What does it do?
Numbered guidelines on transcript reading, stage determination, priority rules, and callback handling.
- Why it matters:
Helps users correctly apply the template and avoid common mistakes.
- What does it do?
Provide a sample JSON response matching the field definitions.
- Why it matters:
Offers a quick reference for the expected output.
9. Insert the Transcript Placeholder
- What does it do?
End with a "## Transcription" header where the actual call log goes.
- Why it matters:
Clearly demarcates where to paste the raw data.
Tips
- Copy the template and fill in the
{{placeholders}} with your specific details.
- Use simple, clear language when defining the call purpose and rules.
- Don’t remove or rename any keys in the JSON snippet, they must match exactly.
Example Prompt Template
## Role
You are a skilled Call Center Operations Analyst specializing in {{Industry}}
operations. You will be given call logs that contain detailed conversation
transcripts between an Agent and a User. The call transcripts could be in
{SUPPORTED_LANGUAGES} or mixed language.
## IMPORTANT
Provide your response strictly in **JSON format** following the specifications
below. Do not introduce any additional fields or custom stage codes beyond those
defined.
## Task
Read the entire conversation transcript carefully and extract the required
information according to the rules. Deliver a structured JSON response containing
only the specified keys.
### Context Understanding
- **Call Purpose**: {{Call Purpose}}
- **Participant Roles**: Agent and User
### Business Context
- **Industry**: {{Industry}}
- **Use Case**: {{Use Case}}
- **Business Rules (Optional)**:
- 1. {{Rule 1}}
- 2. {{Rule 2}}
### Language Processing Guidelines
- **Primary Language**: {{Primary language}}
- **Secondary Languages (Optional)**: {{Secondary language (if applicable)}}
## Specifics
### Field Definitions
```json
{
"STAGE_CODE": ""
}
```
### Allowed Values & Definitions
#### STAGE\\\_CODE Values:
```json
[
{
"code": "ESCALATED_TO_AGENT",
"description": "Transferred to human agent for complex issues",
"criteria": "User requests escalation or dispute is detected."
},
{
"code": "AGREES_FOR_CALLBACK",
"description": "User agrees to a callback",
"criteria": "User explicitly agrees to be called back."
},
{
"code": "DISAGREES_FOR_CALLBACK",
"description": "User declines a callback",
"criteria": "User explicitly declines being called back."
},
{
"code": "BUSY",
"description": "User indicates they are busy",
"criteria": "User says they cannot talk now."
},
{
"code": "FAQ_HANDLED",
"description": "User question answered without escalation",
"criteria": "User asks a routine question and receives answer."
},
{
"code": "NO_INPUT",
"description": "No user response after prompt",
"criteria": "Silence or no intelligible input."
},
{
"code": "INVALID_INPUT",
"description": "Unrecognized or unclear response",
"criteria": "User input is garbled or irrelevant."
},
{
"code": "WRONG_NUMBER",
"description": "Wrong number reached",
"criteria": "User indicates wrong number."
},
{
"code": "DND",
"description": "Do-not-disturb request",
"criteria": "User requests not to be contacted again."
}
]
```
## Critical Analysis Instructions
1. Read the **ENTIRE transcript** before extracting. Don't jump to conclusions
2. **Look for explicit user responses** - Don't assume agreement from silence
## Important Guidelines
### Data Extraction Rules
- **Key Restriction**: Extract only the keys specified in the field definitions.
Do not add any additional keys or fields to the output.
- **Output Compliance**: Follow the exact JSON format specified. The output must
contain only the fields defined in the field definitions section.
## Output Format
Provide response in JSON format.
### Standard Response Structure - json
```
{
"STAGE_CODE": "{EXTRACTED_VALUE}"
}
```
## Transcription
\<transcription\>
