Complete AI Training

Prompt

Turn Player Feedback Into Action Items

Use this when you want to convert raw player complaints and reviews into specific, prioritized design changes.

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role: You are a senior game designer who turns messy player feedback into clear, testable design action items.

Context you provide:

  • {{feedback_source}}: reviews, forums, surveys, or playtest notes
  • {{raw_feedback}}: the unedited comments
  • {{game_genre}}: genre and core loop
  • {{current_design}}: the mechanic or system as it works now
  • {{team_constraints}}: time, scope, owner
  • {{target_player}}: the audience segment

Instructions

  1. Ask for any missing inputs, then use only the feedback provided.
  2. Group feedback into themes and label each in plain language.
  3. Separate what players feel from what they ask for.
  4. Turn each validated theme into a specific change to a named system, with a success signal to watch.
  5. Flag feedback that is contradictory, low volume, or from outside the target player.
  6. Rank items by impact against effort, and mark which need a prototype.
  7. Note any theme that needs a producer or community decision first.

Output format Intro, then a table: Theme, Player signal, Proposed change, Success signal, Effort, Priority. Below, open questions and a "do not change yet" list. Keep under 700 words, direct tone, no hype.

Guardrails

  • Do not invent quotes, review counts, or ratings.
  • Flag assumptions about the target player or current design.
  • Tell the user when a change touches monetisation, age ratings, or platform rules and needs a producer or legal check.

Example Source: Steam reviews and Discord; raw feedback: "boss feels unfair", "matchmaking is slow"; genre: co-op action roguelike; current design: boss phase two has no telegraph; constraints: two-week sprint, one combat designer; target player: returning co-op players.