AI-Assisted UEFI Development: Building a Shell Graphics Game from Scratch

Why UEFI Shell Is a Challenging Test Environment

In conventional application development, programmers rely on mature operating system services such as windowing systems, font libraries, input method frameworks, file system abstractions, and graphics acceleration interfaces. High-level APIs make window creation, text rendering, and event loops straightforward.

UEFI Shell is fundamentally different. It is a pre-boot execution environment with limited system services. Applications must interact with the console, file system, graphics output, and input devices through firmware-provided protocols. Many capabilities that are taken for granted in desktop applications do not exist here and must be implemented manually.

Key constraints include:

1. Graphics drawing may be limited to raw pixel buffer operations.
2. Chinese text rendering typically has no built-in font service.
3. Keyboard input may arrive as scan codes or firmware-level events.
4. File paths and device mapping rules differ from those in general-purpose operating systems.
5. Diagnostic information after a crash is extremely limited.
6. The compilation toolchain, target architecture, and firmware interfaces must be strictly matched.

Because the supporting infrastructure is incomplete, such projects simultaneously test an AI tool’s requirement comprehension, environmental analysis, code organization, and debugging capabilities.

The Goal Should Not Be Merely “Writing Code”

If you simply ask an AI tool to “write a Breakout game,” it will often produce an implementation designed for a conventional desktop environment — complete with window libraries, standard input/output, dynamic resource loading, and other dependencies that do not exist in the target environment.

Therefore, requirements must be decomposed into four layers.

1. Runtime Environment Constraints

The first step is to clearly specify that the program runs in UEFI Shell, not a regular operating system. The following details must be clarified:

• Which firmware development framework is used;
• What the target architecture is;
• What output format the executable should have;
• Whether external resources are allowed;
• How the program is to be validated on a virtual machine or real hardware;
• Which firmware protocols are available at program startup.

2. Functional Scope

The game should be broken down into minimal verifiable features:

• Initialize graphics output;
• Draw the background, paddle, ball, and bricks;
• Read directional or character input;
• Update ball coordinates;
• Detect collisions with boundaries, the paddle, and bricks;
• Remove bricks that have been hit;
• Display score, lives, and game-over state;
• Support restart or exit.

3. Non-Functional Requirements

Low-level programs especially need clear non-functional constraints:

• Do not use operating-system-specific interfaces;
• Do not depend on font libraries whose availability is unconfirmed;
• Avoid unplanned dynamic memory allocation;
• Check all protocol pointers and return values;
• Avoid long blocking waits on input;
• Maintain a compilable version at every stage;
• Output sufficient diagnostic information when errors occur.

4. Acceptance Criteria

Each development stage must have a clear “definition of done.” For example, the graphics initialization stage does not require game logic — merely displaying a solid background and exiting cleanly is sufficient. The input stage only verifies that key presses move the paddle. The collision stage can be tested with fixed coordinates. This staging prevents the problem where all features are integrated at once and failures cannot be localized.

What AI Should and Should Not Do

From a practical perspective, AI tools are not “unattended firmware engineers.” They are better suited for tasks that are repetitive, structurally clear, and require rapid iteration.

Tasks Well-Suited for AI

The first is project skeleton generation. This includes directory structure, build configuration, entry point functions, module partitioning, and basic error handling. The rules here are relatively explicit, and AI can quickly produce a working first pass.

The second is conventional logic implementation. Examples include 2D coordinate updates, rectangle collision detection, brick state management, score tracking, and game state machines. These are largely platform-independent and can be validated independently before being integrated with the low-level environment.

The third is error analysis. By providing the complete compiler output, runtime logs, and observed symptoms, AI can help categorize the error — such as type mismatches, incorrect protocol usage order, misunderstandings of structure fields, or missing link-time dependencies.

The fourth is refactoring. Once the functionality works, AI can be asked to identify duplicate code, resource leaks, boundary conditions, and module coupling to improve readability and maintainability.

Tasks That Should Not Be Fully Delegated to AI

Code involving firmware specifications, hardware behavior, and platform differences requires manual review. This is especially true for protocol acquisition, device paths, graphics mode switching, memory lifecycle management, and input event handling.

AI may generate syntactically correct but semantically incorrect code. For instance, it might assume a protocol is always available, default to a fixed screen resolution, or treat ordinary character encoding as if it were display-ready Chinese text. These errors may surface quickly on a desktop system but could manifest as a black screen, infinite loop, or silent exit in firmware environments.

Therefore, AI-generated low-level code must undergo at least three types of inspection:

1. Whether it conforms to the target specification;
2. Whether all critical return values are checked;
3. Whether assumptions have been validated in the actual runtime environment.

A Phased Development Strategy Is Recommended

Stage One: Establish a Minimal Runnable Program

The first step is not to implement the game itself, but to have the program perform the simplest startup, output, and exit flow. The focus here is to confirm:

• The build toolchain is functional;
• The target file format is correct;
• The Shell can load the program;
• The entry function executes normally;
• Basic console output works.

If this stage fails, subsequent graphics and logic work is meaningless.

Stage Two: Validate Graphics Output

After confirming that the program can launch, acquire the graphics output protocol and draw a solid background and a few rectangles. Use fixed coordinates and fixed colors at this point — do not introduce animation, input, or font rendering simultaneously.

A representative drawing flow can be abstracted as:

Initialize graphics protocol
Get current display mode
Create or prepare pixel buffer
Fill background color
Draw rectangles
Submit buffer
Wait for exit input
Release resources

This stage should focus on the coordinate origin, pixel format, screen boundaries, and buffer layout. Many graphics anomalies are not algorithmic errors but rather issues with color channel order or incorrect row strides.

Stage Three: Add Input and a Main Loop

The game requires a stable main loop, but it cannot simply be an infinite high-speed loop. An excessively fast loop causes high CPU usage, screen flickering, and repeated input triggers; an overly slow loop makes controls feel unresponsive.

A reasonable abstract structure is:

while game is not over:
    read available input
    update paddle position
    update ball position
    perform collision detection
    update bricks and score
    clear or redraw the screen
    output current frame
    wait appropriately

At this point, input collection, game state update, and rendering should be separated into independent modules. This way, replacing the input method or drawing approach later does not require rewriting the entire program.

Stage Four: Complete Collision Detection

Collision detection can start with axis-aligned rectangles. For the ball and bricks, approximate the ball as a small rectangle and determine intersection using boundary relationships.

The common condition is:

left edge of A < right edge of B
right edge of A > left edge of B
top edge of A < bottom edge of B
bottom edge of A > top edge of B

However, merely determining intersection is insufficient — the ball’s rebound direction must also be decided. The simplest implementation uses the ball’s previous position to determine whether the collision occurred on the horizontal or vertical axis. More complex implementations calculate penetration depth to prevent the ball from repeatedly triggering collisions inside a brick.

AI typically performs well on this logic, but developers must pay extra attention to boundary cases:

• The ball hits two bricks simultaneously;
• The ball moves along the paddle edge;
• High ball speed causes tunneling;
• Deleted bricks still participate in collision checks;
• The ball jitters at the screen boundary.

Chinese Text Rendering Is an Easily Underestimated Challenge

In conventional applications, text rendering usually requires only a font API call. In the UEFI Shell environment, however, Chinese text display may require embedding a bitmap font or using pre-generated bitmaps.

There are generally three approaches.

The first is to use the firmware’s built-in text capability, but compatibility depends on the device — fonts, encoding, and display widths may not be consistent.

The second is to embed a small bitmap font library. The program maps each character to a fixed-size bitmap and draws it pixel by pixel. This approach offers strong controllability but requires handling character encoding, glyph storage, and draw scaling.

The third is to pre-render text as bitmap resources. Runtime logic becomes simpler, but resource management complexity increases, and this approach is not suitable for large volumes of dynamic text.

For an experimental project, it is advisable to first complete the main flow using English and numbers, then integrate Chinese as a separate module. This prevents font issues from masking game logic problems.

Compilation and Debugging in a Shell Environment

The most effective debugging approach for low-level projects is not to generate a complete program in one pass, but to establish a short feedback loop of “build → run → observe → fix.”

It is recommended to modify only one problem at a time and document:

• The symptom before the modification;
• The build command used;
• The compiler or linker output;
• The actual behavior on the device;
• Whether new issues appeared after the change.

If validation on real hardware is expensive, a virtualization environment can first verify basic graphics and input logic. However, results from a virtual environment cannot be assumed to hold across all real devices. Different firmware implementations may vary in graphics modes, keyboard input, and file system mapping.

For AI collaboration, error information must be complete. Do not simply say “it failed.” Instead, specify:

• Whether the failure occurred at compile time, link time, or startup;
• Whether there is any screen output;
• Whether the program exits immediately;
• Whether it responds to keyboard input;
• Whether the error is reproducibly stable;
• What changed since the last successful version.

The more structured the information, the more effectively the AI can reason about the problem.

How to Prevent AI From Falling Into Repeated Trial and Error

In complex low-level projects, a common issue with AI is that it keeps patching local code without revisiting overall assumptions. To avoid this, the following methods can be used.

First, require the AI to explain its root-cause attribution before generating new code, rather than directly producing patches. Second, limit the scope of each change and prioritize fixing the earliest occurring error. Third, have the AI maintain a list of environmental facts — recording which interfaces are confirmed to exist, which capabilities are unavailable, and which behaviors have already been verified.

In addition, code should be divided into three categories:

• Platform adaptation layer: protocols, input, output, and resource access;
• Game logic layer: state, collision, and scoring;
• Rendering layer: converting logical state into visual output.

This layering significantly reduces debugging complexity. Even if the platform adaptation layer has temporary issues, the game logic layer can still be tested in a normal development environment.

Conclusions from This Case Study

The value of AI in low-level development is not about fully replacing experienced engineers, but about changing the development process. Previously, developers might have handled everything from spec reading, project scaffolding, interface experimentation, and problem triage. Now, AI can take on a substantial portion of initial implementation and iterative modifications, while engineers focus their effort on architectural judgment, specification review, and final acceptance.

For UEFI, firmware, and hardware-related projects, the most reliable collaboration model is:

1. Humans define boundaries, constraints, and acceptance criteria;
2. AI generates stage-by-stage implementations;
3. Each module is independently verified after completion;
4. Real error information drives the next round of modifications;
5. Platform-related code is manually reviewed;
6. Final testing is performed on real hardware.

Without staged design, AI tends to produce code that looks complete but cannot run. With clear engineering constraints and a structured feedback mechanism, AI can provide substantial assistance in completing complex low-level experiments — even without an existing codebase.

Therefore, when evaluating AI programming capability, one should not only ask whether it can generate clean code in a single pass. More importantly, one should observe whether it can understand environment constraints, continuously absorb error feedback, maintain engineering structure, and proactively validate assumptions under uncertainty. For firmware development, these abilities matter far more than raw code volume.