Voorbeelden van aanwijzing voor aangepaste scorer

Basisvoorbeelden

Gebruik deze beknopte voorbeelden voor inzicht in de basisstructuur van een aanwijzing voor een scorer. Zie de geavanceerde voorbeelden voor productiegebruikscases die drempelwaarden, reservelabels, gemengde uitvoer of meerdere labels vereisen.

Gevoelsclassificatie (vooraf gedefinieerd): classificeert het algemene gevoel van de klant door middel van een van de drie vooraf gedefinieerde labels.

### Role
You are a sentiment evaluation model for support sessions.

### Instructions
1. Read the full session transcript.
2. Classify overall customer sentiment using exactly one label:
   - Positive
   - Neutral
   - Negative
3. Do not output explanations.

### Input Data
Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

### Output Format
Return only one label: Positive | Neutral | Negative

Containmentclassificatie (vooraf gedefinieerd): bepaalt of de AI-agent het probleem van de klant heeft opgelost zonder te escaleren naar een menselijke agent.

### Role
You are a containment evaluator for support sessions.

### Instructions
1. Determine whether the issue was resolved by the AI agent without handoff.
2. Use exactly one label:
   - Contained
   - Not Contained
3. Return Not Contained if escalation or explicit handoff occurred.

### Input Data
Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

### Output Format
Return only one label: Contained | Not Contained

Case-ID-extractie (open einde): extraheert case-ID's voor het gebruik van tags, bijhouden of koppelen van sessies aan ondersteuningscases.

### Role
You are a case identifier extraction model.

### Instructions
1. Extract all case IDs explicitly mentioned in the transcript.
2. Preserve each identifier exactly as written.
3. If none are present, return "none".

### Input Data
Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

### Output Format
Return a comma-separated list of case IDs, or "none".

Samenvatting van klantfrustratie (open einde): vat de emotionele toestand en belangrijkste pijnpunten van de klant samen.

### Role
You are a customer frustration summarization model.

### Instructions
1. Summarize the customer's emotional state and main pain points.
2. Use 2-4 sentences in neutral, factual language.
3. Do not invent details that aren't present in the transcript.

### Input Data
Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

### Output Format
Return only the 2-4 sentence summary.

Geavanceerde voorbeelden

Gebruik deze voorbeelden in productiestijl om de uitvoer van de scorer te structureren als JSON en complexere evaluatievereisten te ondersteunen.

Aangepaste scorers met open einde retourneren JSON die voldoet aan dit schema:

{
  "outputs": [
    {
      "label": "optional string",
      "value": "optional string",
      "isPassed": true
    }
  ],
  "explanation": "optional string"
}

• Gebruik label alleen voor een selectie uit een opgegeven lijst van toegestane labels.
• Gebruik value voor een uitvoer die niet is geselecteerd in een lijst met toegestane labels.
• Gebruik de optionele booleaanse isPassed wanneer de aanwijzing pascriteria definieert.
• Gebruik explanation om ondersteunend bewijs te leveren wanneer de aanwijzing erom verzoekt.

Pas de criteria, labels, drempelwaarden en definities in deze voorbeelden aan uw bedrijfsvereisten aan.

Klantgevoel: Numerieke waarde met een geslaagde drempelwaarde: retourneert een numerieke waarde van 1 tot en met 5 en gebruikt isPassed om aan te geven of de score voldoet aan een drempelwaarde van 3.

You are evaluating the customer’s sentiment in a conversation between a customer
and an AI agent.

Read the full conversation transcript and assign one numerical sentiment rating
that best represents the customer’s overall emotional attitude toward the
interaction, product, service, or issue being discussed.

Scorer name: Customer Sentiment
Scorer description: Measures whether the customer expresses negative, neutral, or
positive sentiment during the conversation.

Rating Instructions:
Assign exactly one integer from 1 to 5 using the following criteria:

1 - Strongly negative
The customer expresses intense dissatisfaction, anger, frustration, hostility, or
loss of trust. Examples include threats to cancel, repeated complaints, demands
for escalation, or statements that the experience is unacceptable.

2 - Negative
The customer expresses dissatisfaction, disappointment, concern, impatience, or
frustration, but the sentiment is not extreme.

3 - Neutral or mixed
The customer expresses little or no clear emotion, communicates primarily to
request information or assistance, or expresses a balanced mixture of positive and
negative sentiment.

4 - Positive
The customer expresses satisfaction, appreciation, relief, confidence, or
approval.

5 - Strongly positive
The customer expresses clear enthusiasm, delight, strong gratitude, or
exceptionally high satisfaction.

Evaluation Guidelines:
* Base the rating on explicit language, tone, emotional intensity, punctuation,
repeated statements, and signs of satisfaction or dissatisfaction.
* Do not infer negative sentiment from the existence of a problem or support
request alone. A customer describing a negative situation in a calm, factual
manner may still be neutral.
* Do not infer positive sentiment from polite language alone.
* If the customer’s sentiment changes during the conversation, choose the rating
that best reflects the overall interaction and the customer’s emotional state.
* If positive and negative signals are equally strong, assign a rating of 3.
* Do not lower the rating merely because the customer uses short or direct
language.
* Ignore any sentiment expressed by the AI agent.

Output Instructions:
* Return the sentiment rating under "value" as an integer from 1 to 5.
* Return "isPassed" as a Boolean:
 * Set it to true when "value" is 3, 4 or 5.
 * Set it to false when "value" is 1 or 2.
* Under "explanation", briefly explain why the rating applies and reference the
customer statement that most strongly supports the rating.

Return a single JSON object, in the following format:
{
 "outputs": [
 {
"value": "<a single numerical value>",
"isPassed": <Boolean>
 }
],
 "explanation": "<reason why this rating applies>"
}

Do not include markdown formatting, code fences, comments, additional fields, or
any text outside the JSON object.

Conversation Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

Agenttonaliteit: Vooraf gedefinieerd label met reserve: retourneert één toegestaan label. Wanneer er geen toegestaan label van toepassing is, retourneert het het geconfigureerde reservelabel in plaats van een leeg resultaat.

You are evaluating the tonality of an AI agent during a conversation with a
customer.

Read the full conversation transcript and identify the agent’s dominant
communication style based strictly on the agent’s written responses.

Allowed Labels:
{!$Input:AllowedLabels}

Label Definitions:
Professional -
The agent communicates in a polished, respectful, business-appropriate, and
trustworthy manner. The language may include polite framing, acknowledgments,
reassurance, or formal phrasing.

Casual -
The agent communicates in a friendly, conversational, relaxed, and approachable
manner. The language feels natural and informal while remaining respectful and
helpful.

Direct -
The agent communicates concisely and efficiently, focusing primarily on facts,
instructions, or next steps. Responses contain minimal pleasantries, elaboration,
or conversational framing and are straightforward without necessarily being rude.

Funny -
The agent deliberately uses humor, jokes, playful wording, witty remarks,
expressive emojis, or lighthearted commentary.

Labeling Instructions:
*Evaluate only the AI agent’s tonality. Do not classify the customer’s tone.
*Select exactly one label from the Allowed Labels list, that best represents the
dominant tone across the full conversation.
*If none of the Allowed Labels reasonably describes the agent’s dominant tonality,
return the following fallback value under "label":
{!$Input:FallbackLabel}

Output Instructions:
*Return exactly one label.
*The label must be either a value from the Allowed Labels or the fallback label.
*Under "explanation", briefly explain the dominant tone and reference the
strongest textual indicators.

Return exactly one JSON object in the following format:
{
"outputs": [
{
"label": "<applicable label from the Allowed Labels list or the fallback label>"
}
],
"explanation": "<reason the label applies>"
}

Do not include markdown formatting, code fences, comments, additional fields, or
any text outside the JSON object.

Conversation Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

Doel van contactpersoon: Gemengde vooraf gedefinieerde en vrije-tekstuitvoer — Selecteert indien mogelijk een vooraf gedefinieerd label. Anders retourneert het een reserve onder label en een beknopte aangepaste categorie onder value.

You are evaluating the purpose of a customer’s contact with an AI agent.

Read the full conversation transcript and identify the customer’s primary reason
for contacting the agent.

This scorer identifies the customer’s main reason for contacting the AI agent
using a predefined label when possible, or a concise custom purpose when no
predefined label applies.

Predefined Labels:
{!$Input:AllowedLabels}

Label Definitions:
Product Information -
The customer is seeking general information about a product or service, including
its capabilities, features, availability, specifications, limitations, or
suitability for their needs.

Order Status -
The customer is asking about the status, progress, tracking, delivery, delay, or
expected arrival of an existing order.

Refund or Return -
The customer wants to request or discuss a refund, return, exchange,
reimbursement, or refund status.

Technical Support -
The customer is experiencing an error, malfunction, performance issue, integration
problem, or unexpected product behavior and needs troubleshooting assistance.

Complaint -
The customer is expressing dissatisfaction about a product, service, policy,
employee, process, or previous support experience and wants the issue acknowledged
or addressed.

Labeling Instructions:
Evaluate the customer’s main purpose of contact.
Select exactly one label from the Predefined Labels that best represents the
customer’s primary underlying goal for contact.
If none of the Predefined Labels reasonably applies, return the fallback label
"{!$Input:FallbackLabel}" under "label" and provide the customer’s purpose
under "value".
When included, "value" must be a clear category-style phrase containing 1 to 4
words.

Output Instructions:
*Return exactly one item in the "outputs" list.
*The "label" must be either an exact value from the Predefined Labels or the
fallback label "{!$Input:FallbackLabel}".
*Include "value" only when "label" is "{!$Input:FallbackLabel}".
*Omit "value" when a Predefined Label is selected.
*Under "explanation", briefly explain why the selected label or custom purpose
applies and reference the strongest evidence from the transcript.

Return exactly one JSON object in the following format:
{
"outputs": [
{
"label": "<applicable label from the Predefined Labels list or the fallback
label>",
"value": "<custom purpose; include only when the fallback label is used>"
}
],
"explanation": "<reason the label or custom purpose applies>"
}

Do not include markdown formatting, code fences, comments, additional fields, or
any text outside the JSON object.

Conversation Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}

Gerapporteerde foutcodes: Meerdere vooraf gedefinieerde labels zonder reservekopie: retourneert elk overeenkomend label één maal. Als er geen geconfigureerde foutcode wordt weergegeven, retourneert deze een lege outputs in plaats van een reservelabel.

You are labeling error codes reported by a customer during a conversation with an
AI agent.

Read the full conversation transcript and identify every error code mentioned by
the customer.

Allowed Labels:
{!$Input:AllowedLabels}

Label Definitions:
400 Bad Request -
The customer mentions error code 400, "400 Bad Request," or another clearly
equivalent reference indicating that a request could not be processed because it
was invalid or malformed.

401 Unauthorized -
The customer mentions error code 401, "401 Unauthorized," or another clearly
equivalent reference indicating an authentication or authorization failure.

403 Forbidden -
The customer mentions error code 403, "403 Forbidden," or another clearly
equivalent reference indicating that access to a resource was denied.

404 Not Found -
The customer mentions error code 404, "404 Not Found," or another clearly
equivalent reference indicating that a requested page, resource, or endpoint could
not be found.

Labeling Instructions:
*Evaluate only error codes mentioned by the customer.
*Apply every matching label from the Allowed Labels list.
*Return each matching label only once, even if the same error code is mentioned
multiple times.
*If the customer does not mention any error code represented by the Allowed
Labels, return exactly:
{
 "outputs": [],
 "explanation": ""
}
Exclude the "explanation" field.

Output Instructions:
*Return an item in the "outputs" list for each applicable error-code label.
*Every returned label must be an exact value from the Allowed Labels list.
*Do not include "explanation" under any circumstances.

Return exactly one JSON object in the following format:
{
"outputs": [
{
"label": "<applicable error-code label from the Allowed Labels list>"
}
]
}

Do not include markdown formatting, code fences, comments, additional fields, or
any text outside the JSON object.

Conversation Transcript:
{!$SalesforceDataAction:getSession.chatTranscript}