Clean a Product Feedback Backlog Before Roadmap Planning
Prepare a large feedback backlog for human review without pretending that a topic count is a roadmap.
Fix the input before you classify it
Export feedback with a stable record ID, date, source, account segment when permitted, and the original text. Remove duplicate exports, internal comments, contact details, and old status labels that will bias the result.
Write a taxonomy that maps to a real follow-up. For example, separate a broken workflow, a missing capability, confusing copy, integration request, and pricing complaint. "Other" is useful. It tells you where the taxonomy needs work.
Test on a labeled sample
Have product and support teammates label a small sample independently. Resolve disagreements before writing the instruction. A model cannot make an unclear classification policy consistent.
Measure agreement by category and inspect false positives. If the workflow mistakes bug reports for feature requests, fix that before you run thousands of records.
Return category, requested_outcome, affected_workflow, evidence_quote, confidence, and needs_human_review.
Allowed categories: defect, missing_capability, usability, integration, pricing, praise, other.
Set needs_human_review to true for security, accessibility, legal, or account-specific issues.Verify this workflow before scaling it
- Input
- Export one feedback source with a stable record ID, date, channel, original text, and only permitted account context.
- Build
- Classify a labeled sample into an action-based taxonomy before sending a JSONL batch.
- Expected result
- Every result keeps an evidence quote and a link to the original feedback record for product review.
- Stop if
- Stop if a defect is treated as a request, a security issue lacks review, or duplicate records inflate a topic.
- Next step
- Use the cleaned themes to prepare a human roadmap discussion, not an automatic priority order.
Use a batch job for work that can wait
DigitalOcean Batch Inference runs a collection of text requests asynchronously. It is a good fit for a backlog refresh or taxonomy test, not the screen an agent is waiting on during a live conversation. Its input is JSONL, and every request needs a unique custom ID.
Submit one representative batch first. Download the result, join it back to the source IDs, and let a product manager filter from category to original message. Never prioritize by generated labels alone.
Turn counts into a review agenda
Counts reveal where to look. They do not prove customer value. Pair volume with customer segment, severity, retention signals, strategic fit, and the cost of a workaround.
Publish the taxonomy version and date next to each report. When the categories change, trend lines need a clear break rather than a false story of product movement.