AhaDo
Sign in
Anchor task

Analyse user feedback with AI

Paste scattered raw feedback in, and AI categorises the issues, tallies the proportions and suggests priorities; you only check the categorisation.

💼 Work · Product manager Several times a month About 2 hours → 10 min

Who does this

Product manager Operations Support lead
How it is usually done
  1. Read every piece of raw feedback one by one
  2. Manually decide the issue type for each
  3. Tally the count and proportion of each type
  4. Write priority suggestions from business experience
✦ The AhaDo solution
  1. Paste the raw feedback (or CSV-exported text)
  2. AI categorises and tallies proportions
  3. Check the categorisation before adopting
  4. Set priority with business judgement

How far AI gets today

L2 · AI draft

AI can give a first cut of the categories and tallies, but a person still needs to check the categorisation, especially edge cases (one piece of feedback spanning several types, or wording too vague to place).

L0
Not advised
L1
AI assists
L2
AI drafts
L4
+ approval
L5
High confidence

Recommended solutions

Simplest · free

Doubao / ChatGPT free tier

Copy the recommended prompt on the right, paste the raw feedback, and generate the categories and proportions.

¥0
See tool guide
Higher quality · paid

Claude / ChatGPT paid tier

A longer context handles more feedback at once and categorises more consistently.

Roughly ¥150-190 per month
See tool guide
✦ Use AhaDo directly

Feedback analyser task app

No tool to switch to and no prompt to write — paste feedback and generate categories, tallies and priority suggestions.

Free to try
Try it now

Recommended prompt

You are a product analysis assistant. Categorise the raw feedback below by issue type (such as bug / usability / performance / feature request / other), tally the proportion of each, and give the top three issues worth prioritising with reasons. Do not invent anything not in the feedback.

Raw feedback:
[paste the feedback here]

Human check points and risks

✓ You must check

  • Whether the categorisation is accurate
  • Whether any feedback was misjudged
  • Whether the priority suggestions match business judgement

⚠ Common risks

  • With too little feedback, the proportions are not meaningful
  • Feedback may contain user privacy details; redact them first
  • AI may merge similar but actually different issues

Usage data

Research draft, not measured performance. The flow and time estimates above are compiled from public methods and not yet verified by real users.
Source type
Research (internal)
Collected on
2026-08-08
Verification status
Awaiting real verification

Corrections and contributions

Used this method? Tell us what is inaccurate, or what you do differently.

Ready to try it once?

Use your own feedback and see whether AI gives a usable categorisation and priority.

Try it now