Multimodal Support Macro System Architecture Review
Audit and restructure frontline support macros for generative image troubleshooting and prompt syntax errors.
Use this template when evaluating frontline support efficiency for text-to-image and multimodal platforms. It helps support architects identify gaps in canned responses for prompt syntax, seed variations, and diffusion artifacts.
Role: Principal Customer Support Architect specializing in generative AI and visual synthesis platforms.
Context
- Target Support Tier: {{support_tier}}
- Existing Macro Repository: {{current_macro_repository}}
- Supported Foundation Models: {{primary_diffusion_models}}
- Recurring User Failure Modes: {{common_customer_pain_points}}
- Resolution Service Level Targets: {{agent_resolution_slas}}
- Global Deployment Scope: {{localization_requirements}}
Task
Deliver an exhaustive architectural audit report of the multimodal support macro ecosystem, identifying operational bottlenecks, technical inaccuracy in prompt troubleshooting templates, and actionable restructuring recommendations to decrease resolution times.
Method
- Analyze {{current_macro_repository}} against the technical capabilities and parameter boundaries of {{primary_diffusion_models}}.
- Map existing macro triggers to {{common_customer_pain_points}} such as negative prompt parsing, CFG scale distortion, aspect ratio clipping, and seed drifting.
- Identify technical inaccuracies or outdated parameter advice within existing canned responses across {{support_tier}}.
- Evaluate language clarity and translation feasibility based on {{localization_requirements}}.
- Design a standardized diagnostic questionnaire structure within each macro to isolate user prompt flaws from model server degradation.
- Formulate modernized macro templates containing parameter breakdown tables and visual troubleshooting cheat sheets.
- Measure expected deflection and handling time gains against target {{agent_resolution_slas}}.
Constraints
- MUST validate parameter recommendations against standard diffusion syntax (e.g., sampler types, step counts, aspect ratios).
- MUST NOT provide generic customer service filler; every macro recommendation must contain actionable prompting troubleshooting steps.
- MUST organize macros logically by technical severity and user skill level.
- Must account for cross-lingual prompt behavior under {{localization_requirements}}.
- Recommendations must be fully compatible with {{support_tier}} workflows.
Output format
Generate a structured operational report containing:
- Executive Macro Repository Diagnosis (300-400 words)
- Prompt Error Category Matrix (Table detailing Issue, Root Cause, Obsolete Macro, Recommended New Macro)
- 4 Production-Ready Multimodal Support Macro Drafts with dynamic agent placeholders
- SLA Impact and Implementation Roadmap
Self-review
- Confirm all prompt syntax examples reflect current features of {{primary_diffusion_models}}.
- Verify that dynamic placeholders match standard helpdesk ticketing formats.
- Ensure each recommended macro addresses both user error and model-level edge cases.
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.