Categorize product feedback with per-topic sentiment

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Paste into a general LLM and assign predefined product-feedback categories, subcategories, sentiment, and evidence q…

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You are an AI assistant trained to categorize user feedback into predefined categories, along with sentiment analysis for each category. Analyze each piece of feedback, assign the most relevant categories, and determine the sentiment (positive, negative, or neutral) associated with each category.

Predefined Categories:
- Product Features and Functionality (Core Features; Add-ons and Integrations; Customization and Configuration)
- User Experience and Design (Ease of Use; Navigation and Discoverability; Visual Design and Aesthetics; Accessibility)
- Performance and Reliability (Speed and Responsiveness; Uptime and Availability; Scalability; Bug Fixes and Error Handling)
- Customer Support and Service (Responsiveness and Availability; Knowledge and Expertise; Issue Resolution and Follow-up; Self-Service Resources)
- Billing, Pricing, and Licensing (Pricing Model and Tiers; Billing Processes and Invoicing; License Management; Upgrades and Renewals)
- Security, Compliance, and Privacy (Data Protection and Confidentiality; Access Control and Authentication; Regulatory Compliance; Incident Response and Monitoring)
- Mobile and Cross-Platform Compatibility
- Third-Party Integrations and API
- Onboarding, Training, and Documentation

Output format:
For each matched category path, list: Category > Subcategory — sentiment — short evidence quote.

FEEDBACK:
<<<paste feedback>>>

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How you would do this use case, in plain words. Anyone can post.

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All use cases:GET /api/jobs
This use case:GET /api/jobs/product-feedback-category-sentiment
Schema:GET /api/jobs/schema
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A bad request returns JSON like {"error":"Method description is required"} with status 400.

Field	What it is
`jobId`	This use case. Review queue uses `useCaseTitle` + `email` instead.
`summary`	Method description. Shown on the card. Also accepts `title`.
`products`	Optional. Array of `{ name, url }`. Legacy: `tools`.
`prompts`	Optional. Array of prompt strings. Legacy: one `prompt` string.
`skills`	Optional. Array of `{ text, url }`. Legacy: `skill` + `skillUrl`.
`authorName`	Author name. Legacy live field: `postedBy`.
`author`	Optional 𝕏 handle. Legacy live field: `creditHandle`.
`parts`	Optional. Inferred from products, prompts, and skills if omitted.`standalone-product`, `ai-agent`, `ai-tool`, `skill`, `prompt`. Do not send a prompt/product/agent/skill radio.

{
  "jobId": "product-feedback-category-sentiment",
  "summary": "Turns a Zoom recording into notes and action items with owners",
  "products": [
    {
      "name": "Claude",
      "url": "https://claude.ai"
    }
  ],
  "prompts": [
    "Turn this transcript into notes and action items with owners."
  ],
  "skills": [
    {
      "text": "Watch a folder. When a transcript lands, write notes with owners.",
      "url": "https://example.com/zoom-notes.md"
    }
  ],
  "authorName": "Alex Rivera",
  "author": "@handle"
}