AI scoring best practices
This guide outlines best practices for creating effective AI-scored evaluation forms in Genesys Cloud Quality Management. It helps ensure every question is clear, objective, and measurable based on the conversation transcript.
AI Scoring evaluates interactions only from the conversation transcript and the evaluation form you provide.
It does not use metadata such as:
- Queue or routing path
- Timestamps or duration
- Platform or system metrics
AI Scoring focuses purely on what was said (or written) — not on tone, attitude, or inferred meaning.
Do
- Write objective, transcript-driven questions.
- Provide clear help text with measurable criteria.
- Use consistent terminology (for example, “agent” and “customer”).
- Write in complete sentences.
- Refine questions continuously based on AI scoring results.
Don’t
- Ask subjective questions (for example, “Was the agent polite?”).
- Use vague shorthand (for example, “Greeting protocol”).
- Assume context not visible in the transcript.
- Ask about tone or emotion that AI cannot infer.
The AI model can’t infer intent. It must find direct, measurable evidence in the transcript.
| Key principle | Details | Why this helps | Example question and help text | Bad example |
|---|---|---|---|---|
| Anchor on transcript-only evidence | Ask only what can be verified from the conversation text. | Keeps questions measurable and prevents false assumptions. | Q: Did the agent greet the customer using a standard phrase such as “hi,” “hello,” or “good morning”? | Did the agent sound friendly when greeting the customer? |
| Make behaviors binary or specific | Use Yes/No or defined multiple-choice answers. | Reduces ambiguity in scoring. | Help text: Yes = Agent greeted the customer using a standard phrase. No = Agent did not greet or used unclear phrasing. | Did the agent greet appropriately? |
| Use complete sentences | Avoid shorthand or partial phrases. | AI interprets full sentences more accurately. | Q: Did the agent confirm the customer’s identity before providing account details? | “Identity verification.” |
| Define scope and boundaries | Explain what counts and what does not. | Prevents misclassification and false positives. | Help text: Count “Can you confirm your date of birth?” Do not count “What’s your name?” unless tied to verification. | Did the agent verify the customer? |
| Make it observable, not emotional | Focus on what can be seen or read. | Keeps evaluations consistent. | Q: Did the agent acknowledge the customer’s issue using a phrase such as “I’m sorry” or “I understand”? | Was the agent empathetic? |
| Standardize terminology | Use the same words across the form. | Improves model consistency. | Always use “agent” and “customer.” | Mixing “rep,” “client,” “associate.” |
| Provide transcript examples | Include short Yes/No examples. | Helps AI recognize real-world phrasing. | Yes: “I’ll refund that charge.” No: “We’ll look into it.” | — |
| Keep each question focused | Test only one behavior per question. | Avoids mixed results. | Q1: Did the agent greet the customer? Q2: Did the agent confirm identity? | Did the agent greet the customer and confirm identity at the start? |
| Anticipate phrasing variations | Write for flexibility, not exact words. | Reduces missed matches. | “Did the agent verify identity using at least one credential (for example, DOB, phone, or account number)?” | “Did the agent ask for the account number?” |
| Align questions with business goals | Tie each question to CX, compliance, or efficiency. | Keeps evaluations meaningful. | “Did the agent provide mandatory disclosure?” | “Did the agent use small talk?” |
Each question should include short, measurable help text (≤500 characters) that defines what counts as Yes or No, includes examples, and clarifies edge cases.
Example: well-written question and help text
Intent: Verify the customer’s identity before providing account-specific details.
Question: Did the agent verify the customer’s identity before providing account-specific details?
Help text: The agent must confirm at least one credential (for example, account number, date of birth, phone number, or order ID).
- Yes: Agent verified a credential before proceeding.
- No: Agent skipped or asked unrelated questions.
Transcript examples
| ✅ | Customer: “I need to update my billing info.” Agent: “Can I confirm the last four digits of your account number?” |
| ❌ | Customer: “I need to update my billing info.” Agent: “Sure, what’s the new address?” |
- Review AI scoring reports monthly to identify low-confidence questions.
- Add examples reflecting your industry’s phrasing (for example, healthcare vs. retail).
- Align human evaluators with the same definitions used by AI.
- Refine questions and help text when AI misclassifies results.
| Error type | When it occurs | How to resolve |
|---|---|---|
| Rate limit error | Daily limit of 50 AI scoring requests per agent reached. | Spread out evaluations, reduce retries, or request a quota increase. |
| Duplicate evaluation error | A form with the same evaluator ID and conversation already exists. | Check for existing evaluations before submission. Use a different form or evaluator ID for re-reviews. |
| Processing failure error | Question design is too vague or complex. | Rewrite questions using transcript-only, measurable behaviors. |
| Low confidence on question | AI is unsure due to ambiguous or subjective phrasing. | Clarify wording, add examples, and standardize terminology. |
AI Scoring Playbook – 10-Question Agent Evaluation Sample Form
This sample evaluation form combines key service fundamentals (for example, compliance, empathy, and efficiency), into objective, transcript-based questions optimized for AI scoring. Each question is designed to measure one observable behavior using consistent phrasing and measurable criteria.
This form demonstrates objective, transcript-based evaluation criteria:
Question:
Did the agent greet the customer at the beginning of the conversation using a standard greeting such as “hi,” “hello,” or “good morning”?
Answer Options:
Yes / No
Help Text:
The agent must begin with a polite greeting. Acceptable greetings include “hi,” “hello,” “good morning,” or equivalent.
Transcript Example:
Customer: “I need help with my account.”
Agent: “Good morning! Thanks for calling ABC Support.”
→ AI marks Yes
Question:
Did the agent verify the customer’s identity before addressing account-specific concerns?
Answer Options:
Yes / No
Help Text:
The agent must verify at least one customer credential, such as date of birth, account ID, or phone number, before proceeding.
Transcript Example:
Customer: “I want to update my billing address.”
Agent: “Can I confirm the last four digits of your account number?”
→ AI marks Yes
Question:
Did the agent allow the customer to finish speaking without interruptions?
Answer Options:
Yes / No
Help Text:
An interruption occurs when the agent cuts the customer off mid-sentence. Clarifications after the customer finishes are acceptable.
Transcript Example:
Customer: “The issue started when—”
Agent: “Let me stop you there…”
→ AI marks No
Question:
Did the agent use the customer’s name at least once during the conversation?
Answer Options:
Yes / No
Help Text:
Using the customer’s name personalizes the interaction. The AI checks whether the name, as provided in the record or conversation, was mentioned.
Transcript Example:
Customer: “I’m Maria, and I need help with my order.”
Agent: “Thank you, Maria. Let’s take a look at your order.”
→ AI marks Yes
Question:
Did the agent acknowledge the customer’s issue before moving to resolution?
Answer Options:
Yes / No
Help Text:
Empathy is demonstrated when the agent acknowledges the issue with phrases such as “I understand,” “I’m sorry that happened,” or “I know this must be frustrating.” The AI looks for acknowledgment before resolution.
Transcript Example:
Customer: “I’ve been overcharged.”
Agent: “I’m sorry to hear that. Let’s review your bill together.”
→ AI marks Yes
Question:
Did the agent provide a clear resolution step, such as a refund, replacement, or troubleshooting instruction?
Answer Options:
Yes / No
Help Text:
A resolution must include a specific action (refund, replacement, or technical steps). General apologies without clear next steps do not count.
Transcript Example:
Agent: “I’ve processed a replacement order that will arrive within two days.”
→ AI marks Yes
Question:
Did the agent explain the escalation process clearly, including who to contact, what information is needed, and expected response times?
Answer Options:
Yes / No
Help Text:
A complete escalation includes:
Next contact point
Required information
Expected timeframe for response
Transcript Example:
Agent: “If this issue persists, I’ll escalate to Level 2. They’ll need your case ID and will respond within 24 hours.”
→ AI marks Yes
Question:
Did the agent comply with mandatory disclosure or compliance statements (for example, terms, disclaimers, or legal requirements)?
Answer Options:
Yes / No
Help Text:
Compliance statements vary by industry and may include required disclosures, disclaimers, or legal notices. AI checks for these specific phrases in the transcript.
Transcript Example:
Agent: “For security purposes, I cannot provide account details without verifying your identity first.”
→ AI marks Yes
Question:
Did the agent avoid unnecessary dead air or long silences without explanation?
Answer Options:
Yes / No
Help Text:
Silences longer than 15 seconds are acceptable only when explained to the customer (for example, “I’ll place you on a brief hold while I check this”).
Transcript Example:
Agent: “Give me a moment while I pull up your account details.” [10 seconds of silence]
→ AI marks Yes
Question:
Did the agent confirm customer satisfaction or summarize next steps before closing the conversation?
Answer Options:
Yes / No
Help Text:
Before closing, the agent should confirm resolution or clearly summarize next steps. This demonstrates completion and helps ensure customer understanding.
Transcript Example:
Agent: “I’ve reset your password. Can you confirm you’re able to log in now?”
→ AI marks Yes
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