AI Prompt Node

The AI Prompt Node lets you interact with large language models (LLMs) directly from your workflow. Use it to parse conversation transcripts, extract structured data from unstructured text, classify intent, or generate dynamic content.

The AI Prompt Node uses the same technology that powers Liberate’s Voice AI - you can leverage LLM capabilities for both real-time conversations and backend workflow processing.

Overview

Use an AI Prompt Node when you need to:

Parse Voice AI transcripts into structured JSON
Extract entities (names, dates, policy numbers) from text
Classify customer intent or claim type
Summarize long conversations
Generate dynamic responses or content
Validate or interpret unstructured input

When to Use AI Prompt vs. Other Nodes

Task	Best Node
Parse unstructured text into JSON	AI Prompt Node ✅
Transform structured JSON to different format	Mapping Node
Simple field extraction with known structure	Mapping Node
Complex text interpretation	AI Prompt Node ✅
Call external API	Task Node
Conditional logic with clear rules	Decision Table Node
Fuzzy matching or intent detection	AI Prompt Node ✅

Use AI Prompt Node for unstructured-to-structured transformations. Use Mapping Node for structured-to-structured transformations.

Configuration

Click on an AI Prompt Node to open its configuration panel.

Basic Settings

Setting	Description
Title	Descriptive name for the node
Prompt	The instruction sent to the LLM
Output Property	Where to store the AI response in context

Writing Effective Prompts

Your prompt should include:

Clear instruction — What you want the AI to do
Input data — Reference to context variables (e.g., {{context.transcript}})
Output format — Specify the exact structure you expect
Examples (optional) — Help the AI understand edge cases

Prompt Structure

Basic Template

[INSTRUCTION]
Describe what you want the AI to do.

[INPUT]
Reference the data to process: {{context.variableName}}

[OUTPUT FORMAT]
Specify the exact JSON structure you expect.

[ADDITIONAL RULES]
Any special handling instructions.

Referencing Context Variables

Use double curly braces to inject context data into your prompt:

Syntax	Description
`{{context.transcript}}`	Insert the transcript field
`{{context.claimant.firstName}}`	Insert nested field
`{{context}}`	Insert entire context object

Insurance Examples

Parse FNOL Transcript

Extract structured claim data from a Voice AI conversation. Prompt:

Parse the following insurance claim call transcript and extract all relevant information as a JSON object.

Extract these fields:
- claimType (water, fire, theft, auto, wind, other)
- claimDescription (brief description of what happened)
- callerFirstName
- callerLastName
- policyNumber (format: XX-XXXXXXXX)
- dateOfLoss (YYYY-MM-DD format)
- timeOfLoss (HH:MM format, 24-hour)
- propertyAddress (full address if mentioned, null if not)
- contactPhone (digits only, null if not mentioned)
- injuriesReported (true/false)

Transcript:
{{context.transcript}}

Respond with ONLY the JSON object, no additional text or explanation.

Sample Input (transcript):

Assistant: Thank you for calling Acme Insurance. How can I help you today?
User: Hi, I need to file a claim. My basement flooded this morning.
Assistant: I'm sorry to hear that. I can help you with that. May I have your policy number?
User: Yes, it's HO-12345678.
Assistant: Thank you. And your name?
User: John Smith.
Assistant: Got it, John. When exactly did the flooding occur?
User: Around 6 AM this morning.
Assistant: Was anyone injured?
User: No, thankfully everyone is fine.

AI Output:

{
  "claimType": "water",
  "claimDescription": "Basement flooded",
  "callerFirstName": "John",
  "callerLastName": "Smith",
  "policyNumber": "HO-12345678",
  "dateOfLoss": "2025-02-17",
  "timeOfLoss": "06:00",
  "propertyAddress": null,
  "contactPhone": null,
  "injuriesReported": false
}

Classify Claim Type

Determine the type of claim from a description. Prompt:

Classify the following claim description into exactly one category.

Categories:
- water (flooding, pipe burst, water damage, leak)
- fire (fire damage, smoke damage, electrical fire)
- theft (burglary, stolen property, break-in)
- auto (car accident, vehicle damage, collision)
- wind (storm damage, hurricane, tornado, fallen tree)
- liability (injury on property, lawsuit, third-party claim)
- other (anything that doesn't fit above categories)

Description: {{context.claimDescription}}

Respond with ONLY a JSON object in this format:
{
  "category": "category_name",
  "confidence": "high/medium/low",
  "reasoning": "brief explanation"
}

Sample Input:

"A tree fell on my car during last night's storm"

AI Output:

{
  "category": "wind",
  "confidence": "high",
  "reasoning": "Storm-related tree damage to vehicle indicates wind/storm claim"
}

Extract Policy Information from Email

Parse an incoming email to extract key details. Prompt:

Extract policy and customer information from the following email.

Email content:
{{context.emailBody}}

Extract:
- customerName (full name)
- policyNumber (if mentioned)
- requestType (quote, claim, billing, policy_change, cancellation, general_inquiry)
- urgency (high, medium, low)
- summary (one sentence describing the request)
- actionRequired (what needs to be done)

Respond with ONLY the JSON object.

Sample Input:

Subject: URGENT - Need to add new driver to policy

Hi, 

My name is Sarah Johnson and I need to add my daughter to my auto policy 
immediately. She just got her license and needs to drive to school starting 
Monday. My policy number is AU-98765432.

Please call me back ASAP at 555-123-4567.

Thanks,
Sarah

AI Output:

{
  "customerName": "Sarah Johnson",
  "policyNumber": "AU-98765432",
  "requestType": "policy_change",
  "urgency": "high",
  "summary": "Customer needs to add new driver (daughter) to auto policy before Monday",
  "actionRequired": "Add new driver to policy AU-98765432 and call customer at 555-123-4567"
}

Summarize Conversation for Agent Handoff

Create a summary for warm transfer to human agent. Prompt:

Summarize the following customer conversation for a human agent handoff.

Include:
- customerName: Name of the caller
- reason: Why they called (one sentence)
- keyDetails: Bullet points of important information gathered
- customerSentiment: positive, neutral, frustrated, or angry
- recommendedAction: What the agent should do next

Transcript:
{{context.transcript}}

Respond with ONLY the JSON object.

AI Output:

{
  "customerName": "John Smith",
  "reason": "Filing a water damage claim for basement flooding",
  "keyDetails": [
    "Policy: HO-12345678",
    "Incident: Basement flooded this morning around 6 AM",
    "Cause: Unknown, possibly pipe burst",
    "No injuries reported",
    "Damage extent not yet assessed"
  ],
  "customerSentiment": "neutral",
  "recommendedAction": "Complete FNOL intake and schedule adjuster visit"
}

Validate and Correct Data

Check extracted data for errors and inconsistencies. Prompt:

Review the following extracted claim data for errors or inconsistencies.

Data to validate:
{{context.extractedClaim}}

Check for:
1. Policy number format (should be 2 letters, dash, 8 digits)
2. Date of loss is not in the future
3. Phone numbers have 10 digits
4. Required fields are not null (claimType, policyNumber, dateOfLoss)

Respond with:
{
  "isValid": true/false,
  "errors": ["list of errors found"],
  "correctedData": { corrected version if fixable, null if not },
  "needsHumanReview": true/false
}

Output Handling

Storing AI Response

Configure the Output Property to specify where the AI response is stored:

Setting	Result
Output Property: `parsedClaim`	Response stored at `context.parsedClaim`

Accessing Output in Subsequent Nodes

After the AI Prompt Node, reference the output in Mapping or Task nodes:

{
  "claimType": $.parsedClaim.claimType,
  "policyNumber": $.parsedClaim.policyNumber
}

Handling JSON Responses

When you instruct the AI to respond with JSON, the output is automatically parsed. You can access nested fields directly:

{
  "fullName": $.aiResponse.callerFirstName & " " & $.aiResponse.callerLastName
}

Prompt Engineering Best Practices

Be Specific About Output Format

❌ Vague:

Extract the claim information from this transcript.

✅ Specific:

Extract claim information as a JSON object with these exact fields:
- claimType (string): water, fire, theft, auto, wind, or other
- dateOfLoss (string): YYYY-MM-DD format
- policyNumber (string): format XX-XXXXXXXX

Respond with ONLY the JSON object, no additional text.

Include Edge Case Instructions

If a field cannot be determined from the transcript, set it to null.
If the policy number format doesn't match XX-XXXXXXXX, include it anyway 
and set "policyNumberFormatValid": false.

Use Examples for Complex Parsing

Example input: "It happened last Tuesday around noon"
Example output: { "dateOfLoss": "2025-02-11", "timeOfLoss": "12:00" }

Example input: "I'm not sure exactly when, sometime last week"
Example output: { "dateOfLoss": null, "timeOfLoss": null, "dateUncertain": true }

Constrain Response Length

For classification tasks, enforce brevity:

Respond with ONLY the category name, nothing else.

For structured data:

Respond with ONLY a valid JSON object. Do not include markdown code blocks, 
explanations, or any text outside the JSON.

Error Handling

AI Response Errors

The AI Prompt Node can fail if:

The LLM service is unavailable
The prompt is malformed
The response doesn’t match expected format

Branching on Errors

Like other nodes, AI Prompt Node has success and error paths:

AI Prompt Node (Parse Transcript)
    ├── Success → Mapping Node → Task Node
    └── Error → Fallback Handler → Escalate to Human

Validating AI Output

Add a Mapping Node after AI Prompt to validate the response:

{
  "isValidResponse": $exists($.parsedClaim.claimType) and $exists($.parsedClaim.policyNumber),
  "parsedClaim": $.parsedClaim
}

Then use a Decision Table or conditional logic to handle invalid responses.

Best Practices

Use AI Prompt for Unstructured Data Only

If your input is already structured JSON, use a Mapping Node instead—it’s faster and more predictable.

Keep Prompts Focused

One AI Prompt Node should do one thing well:

❌ Bad	✅ Good
Parse transcript AND classify claim AND generate summary	AI Prompt 1: Parse transcript
	AI Prompt 2: Classify claim type
	AI Prompt 3: Generate summary

Test with Edge Cases

Test your prompts with:

Incomplete information
Misspellings and typos
Unusual formats
Multiple languages (if applicable)
Very short or very long inputs

Monitor Output Quality

Regularly review AI Prompt outputs in production:

Are extractions accurate?
Are classifications correct?
Are there patterns in failures?

Refine prompts based on real-world performance.

Common Issues

AI Returns Extra Text

Symptom: Response includes explanations or markdown instead of just JSON. Fix: Add explicit instructions:

Respond with ONLY the JSON object. 
Do not include any explanations, markdown formatting, or text outside the JSON.

Inconsistent Output Format

Symptom: Sometimes returns "date": "02/17/2025", sometimes "date": "2025-02-17". Fix: Specify exact format in prompt:

dateOfLoss (string): Must be in YYYY-MM-DD format. Example: "2025-02-17"

Null Values Not Handled

Symptom: AI omits fields instead of setting them to null. Fix: Explicitly instruct:

If any field cannot be determined, include the field with value null.
Never omit fields from the response.

Slow Response Times

Symptom: AI Prompt Node takes several seconds. Fix:

Shorten the prompt
Reduce input data size (truncate long transcripts)
Use async workflow execution

Next Steps

Voice AI Workflows

See AI Prompt in action parsing transcripts

Mapping Node

Transform AI output for external systems

Task Node

Send parsed data to your systems

Decision Table Node

Route based on AI classifications

​AI Prompt Node

​Overview

​When to Use AI Prompt vs. Other Nodes

​Configuration

​Basic Settings

​Writing Effective Prompts

​Prompt Structure

​Basic Template

​Referencing Context Variables

​Insurance Examples

​Parse FNOL Transcript

​Classify Claim Type

​Extract Policy Information from Email

​Summarize Conversation for Agent Handoff

​Validate and Correct Data

​Output Handling

​Storing AI Response

​Accessing Output in Subsequent Nodes

​Handling JSON Responses

​Prompt Engineering Best Practices

​Be Specific About Output Format

​Include Edge Case Instructions

​Use Examples for Complex Parsing

​Constrain Response Length

​Error Handling

​AI Response Errors

​Branching on Errors

​Validating AI Output

​Best Practices

​Use AI Prompt for Unstructured Data Only

​Keep Prompts Focused

​Test with Edge Cases

​Monitor Output Quality

​Common Issues

​AI Returns Extra Text

​Inconsistent Output Format

​Null Values Not Handled

​Slow Response Times

​Next Steps

Voice AI Workflows

Mapping Node

Task Node

Decision Table Node

AI Prompt Node

Overview

When to Use AI Prompt vs. Other Nodes

Configuration

Basic Settings

Writing Effective Prompts

Prompt Structure

Basic Template

Referencing Context Variables

Insurance Examples

Parse FNOL Transcript

Classify Claim Type

Extract Policy Information from Email

Summarize Conversation for Agent Handoff

Validate and Correct Data

Output Handling

Storing AI Response

Accessing Output in Subsequent Nodes

Handling JSON Responses

Prompt Engineering Best Practices

Be Specific About Output Format

Include Edge Case Instructions

Use Examples for Complex Parsing

Constrain Response Length

Error Handling

AI Response Errors

Branching on Errors

Validating AI Output

Best Practices

Use AI Prompt for Unstructured Data Only

Keep Prompts Focused

Test with Edge Cases

Monitor Output Quality

Common Issues

AI Returns Extra Text

Inconsistent Output Format

Null Values Not Handled

Slow Response Times

Next Steps