Vibe coding — the practice of describing what you want in natural language and letting AI generate the implementation — has become one of the most discussed workflows in game development. Proponents claim it democratizes development. Critics call it a recipe for unmaintainable spaghetti. The truth, as usual, sits somewhere in between.

We decided to test vibe coding under realistic conditions. Not a demo. Not a tutorial snippet. A complete, shippable game mechanic: a grappling hook system for a third-person action game in Unreal Engine 5.7. The kind of system that touches physics, animation, input handling, camera behavior, and level interaction simultaneously.

We documented every step — what the AI generated correctly, what it got wrong, where we had to intervene, and how long the whole process took compared to traditional implementation. This post is that documentation.

The Setup

What We Built

The grappling hook system includes:

• A targeting system that identifies valid grapple points within range and line of sight
• A launch mechanic with a cable visual that extends from the player to the grapple point
• Physics-based swinging using Unreal's constraint system
• Mid-air release with momentum preservation
• Camera adjustments during the swing (wider FOV, adjusted follow distance)
• Animation blending between swing, release, and landing states
• A cooldown system with UI feedback
• Edge cases: what happens when the grapple point is destroyed mid-swing, when the player takes damage while swinging, when two grapple points are equidistant

The Rules

We committed to vibe coding as much as possible:

1. Describe what we want in natural language
2. Let the AI generate the implementation
3. Only intervene when something is demonstrably broken
4. Document every intervention
5. Use the Unreal MCP Server to execute operations directly in the editor

We used Claude as the AI assistant, connected to the Unreal MCP Server for editor operations and direct code generation.

The Baseline

For comparison, we estimated that a mid-level Unreal developer would need 3-5 days to implement this system from scratch, including iteration and basic testing. A senior developer who had built similar systems before might do it in 2-3 days.

Phase 1: Scaffolding (Where Vibe Coding Excels)

The Prompt

Our first prompt was deliberately high-level: "Create a grappling hook component for a third-person character. It should detect valid grapple points in front of the player, launch a cable visual, swing the player using physics, and handle release with momentum preservation."

What the AI Generated

The AI produced a surprisingly complete initial scaffold:

• A UGrappleHookComponent with proper UCLASS and UPROPERTY declarations
• A trace-based targeting system using sphere traces
• A cable component for the visual
• A physics constraint for the swing
• Input binding setup
• Basic state machine (Idle, Targeting, Launching, Swinging, Releasing)

The boilerplate was excellent. UCLASS specifiers were correct. UPROPERTY macros had appropriate categories and edit conditions. The component hierarchy was sensible. The state machine pattern was clean and extensible.

Time: 15 minutes from prompt to compilable code in the editor.

What Was Right

The AI excels at structural code — the kind of implementation where there is a well-established pattern and the challenge is getting the details right. Component setup, property declarations, function signatures, state machine skeletons — this is the territory where AI saves the most time.

It also correctly identified that a grappling hook should be a component rather than baked into the character class. This architectural decision is correct but not obvious to beginners. The AI appears to have internalized common Unreal patterns from its training data.

The targeting system used UKismetSystemLibrary::SphereTraceMultiForObjects with appropriate parameters. The collision channel setup was correct. The line-of-sight check used a separate line trace to verify the target was not occluded. This is the standard approach, and the AI implemented it without being told the specific technique.

What Needed Minor Fixes

The component registration order had a subtle issue — the cable component was attached to the character mesh rather than the scene root, which would cause it to deform with animations. A quick fix, but the kind of thing that would not show up until you tested with animated characters.

The state machine used an enum but did not include a Cooldown state, despite cooldown being a standard feature of action abilities. We added it with a one-line prompt.

Assessment: Excellent. The scaffolding phase is where vibe coding delivers its strongest value. What would take an experienced developer an hour of boilerplate writing took 15 minutes.

Phase 2: Physics Behavior (Where Things Get Interesting)

The Prompt

"Implement the swing physics. When the player launches the grapple, they should swing like a pendulum from the grapple point. The cable should have a fixed length equal to the distance at launch time. Gravity and player input should affect the swing."

What the AI Generated

The AI set up a UPhysicsConstraintComponent between the player capsule and the grapple point, configured as a ball-and-socket joint with the correct linear limits.

What Went Wrong

Three issues emerged during testing:

Issue 1: The constraint was too stiff. The AI set the constraint's linear limit stiffness to a very high value, which made the swing feel like the player was attached to a rigid pole rather than a cable. The swing had no elasticity and felt unnatural. The AI had no intuitive sense of what "good swing feel" means because that is a game feel judgment, not a technical specification.

Issue 2: Player input during the swing was applied as direct velocity changes rather than forces. This created jerky, unrealistic movement instead of smooth swing augmentation. The AI implemented the technically correct approach (add velocity) rather than the game-feel correct approach (apply forces that interact with the pendulum physics).

Issue 3: The momentum calculation on release was wrong. The AI preserved the player's velocity at the moment of release, but it did not account for the angular velocity of the swing. A player releasing at the bottom of a swing arc should launch forward with significant speed. The implementation just used the capsule's linear velocity, which was mostly downward at that point.

The Fix Process

Issues 1 and 2 were parameter tuning problems that we resolved through iterative prompting: "The swing feels too stiff, reduce the constraint stiffness and add some damping." "Change the input from velocity changes to force application." Each prompt produced correct changes on the first try.

Issue 3 required actual engineering intervention. We tried several prompts:

• "Fix the release momentum to account for swing velocity" — the AI adjusted the calculation but still used linear velocity
• "Calculate the tangential velocity of the pendulum at the release point" — the AI generated a formula that was mathematically correct for a simple pendulum but did not account for the 3D constraint
• "Use the angular velocity of the swing to calculate launch direction and speed" — this produced working code, but only after we specified the exact vector math

Time: 2 hours, including testing and iteration. A developer who understood pendulum physics would have gotten this right in 30-45 minutes. The AI cost us extra time because we had to diagnose the problem, figure out the correct approach, and then explain it precisely enough for the AI to implement it.

The Lesson

Vibe coding works well when the AI can pattern-match against common implementations. Swing physics for grappling hooks exist in many games, but the specific implementation details — constraint parameters, force application, momentum transfer — require game-specific tuning that AI cannot derive from a description alone.

This is not a failure of AI. It is a fundamental limitation of natural language as a specification for physical behavior. "It should feel like swinging" is not a precise instruction. The developer still needs to understand the physics and articulate specific changes.

Phase 3: Camera and Animation (Where Vibe Coding Is Adequate)

Camera Adjustments

We prompted: "During the swing, smoothly widen the camera FOV by 10 degrees and increase the follow distance by 150 units. Revert when the swing ends."

The AI generated a timeline-based interpolation using FMath::FInterpTo that smoothly transitioned the camera parameters. This was clean, correct, and needed no modification. Camera parameter interpolation is a well-trodden path with clear patterns.

Animation Blending

We prompted: "Blend from the locomotion state to a swing animation during the grapple. Use an animation montage for the launch, then blend to a looping swing pose. On release, play a release montage and blend back to locomotion."

The AI generated montage playback calls and blend logic that was structurally correct but had two issues:

Issue 1: The blend times were uniform. Launch, swing, and release all used the same 0.2-second blend time. In practice, the launch should be snappy (0.1s), the swing blend should be smooth (0.3s), and the release should match the momentum (0.15s). This is a design judgment, not a technical problem.

Issue 2: The AI did not handle the case where the player releases during the launch montage. If the player taps and releases the grapple button quickly, the launch montage was still playing when the release logic triggered, causing a visual pop. We had to prompt specifically for montage interruption handling.

Time: 45 minutes. Comparable to traditional implementation.

Phase 4: Edge Cases (Where Vibe Coding Fails)

This is where the 45% vulnerability statistic becomes real. Studies of AI-generated code consistently find that around 40-50% of generated code contains security vulnerabilities, logic errors, or unhandled edge cases. Our grappling hook confirmed this pattern.

Edge Case 1: Grapple Point Destroyed Mid-Swing

We prompted: "Handle the case where the grapple point actor is destroyed while the player is swinging."

The AI generated a validity check on tick that was correct in concept but used IsValid() instead of IsValidLowLevel() combined with a weak object pointer. In Unreal, IsValid() on a raw pointer after the object is destroyed can crash. The AI needed to store a TWeakObjectPtr and check IsValid() on that.

This is exactly the kind of engine-specific gotcha that AI frequently misses. The pattern looks correct to someone who does not know Unreal's memory model. In production, it would be a crash bug that only triggers under specific timing conditions.

Edge Case 2: Overlapping Grapple Inputs

The AI did not handle rapid input spam. Pressing the grapple button repeatedly during the launch phase could trigger multiple grapple sequences simultaneously, creating visual chaos and physics instability. We had to explicitly prompt for input locking during state transitions.

Edge Case 3: Network Replication

We prompted: "Make this system work in multiplayer with network replication."

The AI's response was ambitious but deeply flawed. It attempted to replicate the physics constraint, which is not how Unreal's networking model works for physics objects. The correct approach is to replicate the grapple state and target, then let each client simulate the physics locally with correction. The AI generated code that would compile but produce desynchronized, jittery results in any real multiplayer test.

We abandoned vibe coding for the networking implementation and wrote it manually.

Edge Case 4: Collision During Swing

What happens when the swinging player collides with a wall? The AI's initial implementation did nothing — the player would clip through geometry because the physics constraint did not account for the capsule's collision during the swing. We had to implement cable shortening on wall contact, which required custom trace logic that the AI could not produce from a natural language description.

Time for all edge cases: 4 hours. At least 2 hours of that was diagnosing problems in AI-generated code and figuring out that the AI's solutions were subtly wrong. A developer who anticipated these edge cases from experience would have handled them as part of the initial implementation.

The Scorecard

Where Vibe Coding Excelled

• Scaffolding and boilerplate: 90% reduction in time for component setup, property declarations, and basic structure
• Standard patterns: Targeting systems, state machines, camera interpolation — anything with well-established implementation patterns
• Iteration speed: Small changes and parameter adjustments were faster through natural language than manual editing
• Discovery: The AI suggested approaches (like using a physics constraint instead of manual pendulum math) that a less experienced developer might not have known about

Where Vibe Coding Produced Bugs

• Physics tuning: AI has no concept of game feel. Every parameter needed manual adjustment
• Engine-specific patterns: Unreal's memory model, replication system, and animation framework have nuances that AI frequently gets wrong
• Edge cases: The AI addressed the happy path well but consistently failed to anticipate failure modes
• Complex math: The momentum calculation required the developer to understand the physics and guide the AI to the correct solution

Total Time

• Vibe coding approach: approximately 8 hours
• Estimated traditional approach (mid-level developer): 3-5 days (24-40 hours)
• Estimated traditional approach (senior developer): 2-3 days (16-24 hours)

The time savings are real, but they come with a quality caveat. The vibe-coded version required an additional 3-4 hours of review, testing, and fixing to reach the same quality level as a traditionally-implemented system. The net savings are still significant — roughly 50-60% for a developer who knows what to look for.

The Review-and-Fix Workflow

Based on this experiment, here is the workflow we recommend for vibe coding game mechanics:

Step 1: Scaffold with AI

Use vibe coding for the initial structure. Component hierarchy, state machines, property declarations, input bindings — let the AI generate all of this. It is fast and usually correct.

Step 2: Implement Core Logic with Guided AI

For the primary mechanic (the swing physics, in this case), use AI but be prepared to guide it. Describe the desired behavior, test the result, and iterate with specific technical corrections. Do not accept the first output without testing.

Step 3: Edge Cases — Write or Heavily Guide

Edge cases are where AI-generated code is most dangerous. Either write edge case handling manually, or prompt the AI with extremely specific scenarios and verify every line of the output. Do not assume the AI has considered failure modes.

Step 4: Review Everything

Before calling the mechanic complete, review every function the AI generated. Look specifically for:

• Unreal-specific memory management issues (raw pointers to UObjects, missing null checks)
• Missing network replication considerations
• Unhandled state transitions
• Performance issues (operations in Tick that should be event-driven)

Step 5: Consider Pre-Built Alternatives

For common game systems — inventory, dialogue, abilities, save/load — vibe coding from scratch may not be the most efficient approach. The Blueprint Template Library provides eight production-tested gameplay systems that have already been through the kind of edge case handling and optimization that vibe coding struggles with. Starting from a vetted implementation and customizing it is often faster and more reliable than generating a system from scratch.

This is not a sales pitch — it is a practical observation. The time we spent debugging AI-generated edge case handling for the grappling hook could have been avoided if a production-ready grappling hook component existed. For systems where vetted implementations are available, using them as a foundation is the pragmatic choice.

When to Vibe Code and When Not To

Vibe Code When

• You are prototyping and speed matters more than code quality
• The system follows well-established patterns that AI has likely seen in training data
• You understand the domain well enough to catch AI mistakes
• The cost of a bug is low (can be caught in playtesting, not a shipping blocker)

Do Not Vibe Code When

• The system involves complex physics or math that requires precise implementation
• Network replication is required (AI consistently struggles with Unreal's networking model)
• The system is safety-critical or crash-prone (save systems, memory management, async loading)
• You do not understand the domain well enough to review the output

Hybrid Approach

The most effective workflow combines vibe coding for speed with traditional development for quality. Use AI to get 70% of the way there fast, then use your engineering judgment for the remaining 30% that makes it production-ready.

The Unreal MCP Server supports this hybrid approach well. You can use it to rapidly scaffold components and configure properties through natural language, then switch to manual editing for the critical logic paths. The AI handles the boring parts so you can focus your attention on the parts that matter.

Honest Conclusions

Vibe coding is a real productivity tool, not just a trend. For game mechanics, it reduced our implementation time by roughly 50-60% compared to traditional development. That is significant.

But it is not a replacement for engineering judgment. The 45% vulnerability rate in AI-generated code is not a number to dismiss. Every line of AI-generated code needs review. Edge cases need manual attention. Game feel cannot be specified in natural language.

The developers who get the most from vibe coding are, counterintuitively, the ones who need it least — experienced developers who can quickly identify and fix AI mistakes. For beginners, vibe coding is seductive but dangerous. You get working code fast, but you do not develop the understanding needed to maintain and debug it when things go wrong.

Our recommendation: use vibe coding as an accelerator, not a crutch. Learn the fundamentals. Understand the systems you are building. Then use AI to build them faster. That is the workflow that actually ships games.