Gains:
- Ability to understand the concepts of core loop, mechanics and MVP and use artificial intelligence for mechanical idea reproduction and prototype skeleton.
- Ability to make quick and cheap iterations by narrowing the scope by understanding that the prototype must answer a single test question
- Being able to recognize that whether a mechanic is fun or not can only be understood by playing it in hand and verifying the test hypothesis, and that the polish trap should be avoided.
Every game starts as an idea; But ideas are cheap, playable prototypes are expensive. Prototype; It is a playable blueprint for testing whether a mechanic is fun in the quickest and cheapest way, without polished graphics or full content. The harsh truth of game design is this: it's all too common for a mechanic to look great on paper but feel terrible in the hand. Therefore, success is not about "finding the right idea", but about quickly eliminating bad ideas and doing many iterations until you find the good one. That's where AI comes in: it speeds up idea generation, rule writing, and prototyping, giving you more chances to experiment.
In this unit, you will learn how to use AI end-to-end in mechanical design and rapid prototyping; You will learn how to constrain the idea, clarify the core loop, and make the prototype testable.
Core loop: the center of everything
The heart of a game is the core loop — the chain of actions that the player performs over and over again, forming the basis of the game. For example, in a shooter "explore → combat → collect → evolve"; "plant → grow → harvest → sell → raise" in a farm game. In the prototype stage, your only goal is to understand whether this cycle is fun or not. Use AI as a thinking partner here that clarifies the core loop: describe your loop to it, ask for the weak link, ask for alternative loops.
A step by step flow:
- Define the constraint. Platform, genre, audience, session duration, control scheme. The clearer the constraint, the more useful the idea.
- Write the core loop. Express the cycle with 4-6 verbs; Ask the AI to strengthen this.
- Generate mechanical variations. Ask for different mechanical options for each cycle step.
- Reduce prototype scope. What is "minimum testable version" (MVP)? Ask the AI what can be extracted.
- Set up a test hypothesis. Write the sentence "This prototype should prove".
Hint: The purpose of the prototype is not to "make the game" but to "answer a question." Always ask the AI “what question should this prototype test”; the scope automatically shrinks.
Paper prototype and digital prototype
AI is helpful in both types of prototypes. The paper prototype (draft played with tabletop rules, card-dice) tests the logic of the mechanics without writing code. You can ask the AI to generate the ruleset, card texts, possible imbalances on a table basis. For the digital prototype, the AI can generate skeleton code (a player controller, a simple state machine) that will run within the engine; We will deepen this in unit 7. At the prototype stage, expect a draft that "works enough to test feel" from the AI-generated code, not polish.
Caution: Do not waste time on beautiful visuals and music in the prototype. “Polish trap” — favor an ugly but fun prototype over a beautiful but boring one. Focus on testing the mechanics rather than asking the AI for visuals.
Iteration speed and "courage to kill"
The secret skill of prototyping is being able to kill a bad idea quickly. Designers become emotionally attached to their own ideas; If you've spent two weeks on a mechanic, it becomes difficult to abandon it even if you see that it's bad — this is called the "sunk cost fallacy" (the tendency to persist with a bad path because of the effort expended). AI provides a hidden advantage here: idea generation and scaffolding become so cheap that throwing away a prototype is now a waste of hours, not days. This cheapness makes you brave. The rule is: put a “success/failure benchmark” and a “decision date” on each prototype — “if the combat feel isn't fun after two days, we cut it.” You can also consult the AI in evaluating this criterion, asking "what signs indicate that this mechanic is not working?"
Another practical: Use AI as a devil's advocate. Tell him about a mechanic you like and say "brutely tell him the three weakest aspects of this idea and why it might fail." This way you see early the weaknesses that have been blinded by your own enthusiasm. The aim is not to defend the idea, but to test it; An idea that passes testing is worthy of a prototype.
three mini cases
Case 1 — Once the cycle became clear, the idea settled. A designer had vaguely defined the core loop of a card-battle game. He had the AI write the loop with 5 verbs and asked "which is the weakest link?" The AI flagged that the "collect" step does not present decisions to the player. The designer added a selection to this step; In playtest, the average session time increased from 4 minutes to 11 minutes.
Case 2 — Quick elimination. One studio received 15 variations from AI for a platforming mechanic; He evaluated 15 of them on paper in 2 hours, prototyped 4 of them, and selected 1. Without AI, they would normally try 3 variations and settle for one; Wider search brought better mechanics.
Case 3 — Returning from the scope trap. One team wanted to add inventory, a quest system, and dialogue to the initial prototype. They asked the AI “what single question should this prototype test?” The answer was "Is the feeling of combat fun?" They took out everything else; The prototype came out in 4 days instead of 3 weeks and answered the right question.
Four copyable templates
1) Core loop strengthening:
Your role: senior game designer. My game: [genre, platform, audience, session duration]. My core loop: [4-6 verbs]. Task: (1) name the weakest link in this loop and why, (2) evaluate whether a decision makes sense to the player at each step, (3) suggest 3 concrete changes that will strengthen the loop.
2) Mechanical variation generation:
For that core action [e.g. "place block"] generate 10 truly different mechanical variations. Write one sentence for each: the rule, the decision it presents to the player, the possible risk. Eliminate the clichés (known to everyone).
3) MVP scope reduction:
I will make a first prototype of the following game idea. Tell me (1) the ONE question that this prototype must prove, (2) the minimum list of mechanics that are absolutely necessary to test that question, (3) everything that can be extracted from the first version. Idea: [write here]
4) Paper prototype rules:
I want to test the following mechanics on desktop (with cards/dice/tokens) without writing code: [mechanics]. Give me a playable paper prototype rule set, the necessary components, and 3 signs to look for during testing.
Weak prompt / Strong prompt
Weak prompt:
Design me a fun mechanic.
"Fun" cannot be measured; There is no context. AI proposes a general mechanic.
Powerful prompt:
My game: PC, roguelike, 20-30 minute rounds, target audience: hardcore. Core cycle: explore-fight-get stronger-die-start again. Problem: players get bored after the 3rd round (feeling repetitive). Task: suggest 5 mechanics that will make each round feel different, that do not give permanent progress. For each: explain how it adds variety and how it might upset the balance.
The concrete problem (“boredom after round 3”) and the constraint make the output directly applicable.
Prototype type comparison chart
Size
paper prototype
digital prototype
speed
Very fast (hours)
Medium (days)
tested
rule/logic
Feel, tempo, control
AI contribution
Rule, card text, balance
Skeleton code, state machine
Cost
low
medium
border
Cannot test feel/tempo
Code effort required
Common mistakes
- Skipping the core loop. If the cycle is not clear, no mechanics will fit.
- Polishing the prototype. Visual/audio is a waste of time in the prototyping phase.
- Inflating the scope. The prototype should test one question; Not "everything".
- Commitment to a single idea early. Producing and eliminating large quantities with AI gives better results.
- Not establishing a test hypothesis. "What am I proving" or the prototype result cannot be interpreted.
In summary
Prototyping is the art of eliminating bad ideas cheaply. In this process, AI multiplies ideas, sharpens the core cycle, helps you narrow the scope, and speeds up the paper/digital prototype skeleton. But you can only answer the "is it fun" question by playing the hand and verifying your test hypothesis. Produce widely, handle quickly, test a single question.
Application task
Choose a game idea. Write the core loop with 5 verbs and learn the weakest link from AI with the "Core loop reinforcement" template. Then identify the single question the prototype should test with the “MVP scope reduction” template. Summarize the result in 5 lines.
checklist
- [ ] I wrote the core loop with clear verbs.
- [ ] At each loop step, I checked whether there were any meaningful decisions for the player.
- [ ] I determined the single question that the prototype would test.
- [ ] I narrowed the scope to MVP, added no polish.
- [ ] I eliminated by producing wide variations and playing in hand.