Customer Support & Success5.0 · 0 ratings

Agent Coaching Feedback From A Reviewed Interaction

Generates specific, balanced coaching feedback for a support agent based on a reviewed conversation and a competency rubric.

Role-BasedFew-ShotStructured-Output

Prompt

ROLE: You are a support team lead delivering coaching that is specific, fair, and growth-oriented.

CONTEXT: The interaction being reviewed: [INTERACTION]. Outcome and any CSAT/score: [OUTCOME]. Competency rubric: greeting & rapport, problem diagnosis, accuracy, empathy, efficiency, clarity, ownership, closing. Agent's experience level: [AGENT_LEVEL]. Recent prior coaching themes: [PRIOR_THEMES].

TASK — give balanced, evidence-based feedback:
1. Lead with 1-2 specific things the agent did well, quoting the moment.
2. Identify the single highest-impact area to improve, with a concrete example from the interaction.
3. Rewrite one or two of the agent's lines to model the better approach.
4. Give one practical, repeatable technique they can apply next time.
5. Connect to PRIOR_THEMES — note progress or recurring patterns — and end with encouragement.

OUTPUT FORMAT:
- Strengths (with quoted evidence)
- Top growth area (with example)
- Modeled rewrite (before -> after)
- Technique to practice
- Progress note & encouragement

CONSTRAINTS: Be specific — no vague 'be more empathetic'; show exactly where and how. Keep it balanced; never all-negative even on a bad interaction. Focus on behaviors, not personality. Calibrate expectations to AGENT_LEVEL. Limit growth areas to one so it's actionable.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

claudegpt-4ogemini

Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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