Can AI Write UEFI Code? A Hands-On Test Building a UEFI Breakout Game

Introduction: AI Coding in a Runtime-Free UEFI Environment

AI-powered coding assistants have become a staple of everyday software development. But how well do they perform in bare-metal scenarios with no operating system, no graphics framework, and no standard library?

The answer is not a simple yes or no. This article uses the development of a graphical Breakout game with Chinese text support under UEFI Shell as a benchmark to deliver a quantitative assessment and a reproducible practice path.

Key takeaway up front: AI can efficiently generate code scaffolding and common algorithms, but low-level protocol details and non-standard data formats still need human oversight.

What Makes UEFI Game Development So Hard?

UEFI (Unified Extensible Firmware Interface) has replaced the traditional BIOS. Its Shell provides a command-line interface, but the constraints compared to a normal development environment are severe:

• No standard graphics library: All drawing must manipulate the framebuffer directly through the UEFI GOP protocol.
• Limited memory management: No garbage collection — everything must be manually allocated and freed.
• Chinese text rendering is difficult: UEFI’s native character set is limited, so you must implement your own bitmap font rendering.
• No audio or networking: Both subsystems are unavailable.

The project goal is clear: produce a compilable .efi executable with GOP-based graphics, Chinese bitmap fonts, collision detection, and a state machine — without an audio module.

Important warning: modifying the EFI partition carries a risk of bricking your system. Always test in a virtual machine and back up your boot partition before starting.

Tool Selection: Model Version and Parameters Matter

The experiment uses a mainstream cloud-based large language model (v2.5, temperature 0.7, max tokens 4096) for code generation and assisted development. The reasoning: top-tier models offer strong reasoning but at high cost, while daily iteration demands a more economical and stable option. Future work will include cross-model comparisons on the same platform for a multi-dimensional evaluation.

Hands-On: From Project Init to Compilable Code

Project Setup and Build Environment

The experiment uses EDK2 (v202405) as the development framework with the GCC-EFI toolchain (x86_64-w64-mingw32-gcc 13.2) for cross-compilation. Build commands:

# Initialize the workspace
source edksetup.sh BaseTools
# Build the .efi binary
build -a X64 -t GCC5 -p AppPkg/AppPkg.dsc

At project initialization, a single command creates a configuration file that defines the development environment, application type, and feature scope. Think of it as a “project specification” that provides persistent context for every subsequent conversation round.

Graphics Rendering: Verified, Compilable GOP Initialization Code

Below is a validated GOP initialization snippet (replacing the original pseudocode):

#include <Uefi.h>
#include <Protocol/GraphicsOutput.h>
EFI_GRAPHICS_OUTPUT_PROTOCOL *gGop;
EFI_STATUS Status;
Status = gBS->LocateProtocol(
    &gEfiGraphicsOutputProtocolGuid,
    NULL,
    (VOID **)&gGop
);
if (EFI_ERROR(Status)) {
    Print(L"Failed to locate GOP: %r\n", Status);
    return Status;
}
UINT32 ModeNumber = gGop->Mode->Mode;
Status = gGop->SetMode(gGop, ModeNumber);
if (EFI_ERROR(Status)) {
    Print(L"Failed to set video mode: %r\n", Status);
    return Status;
}
EFI_GRAPHICS_OUTPUT_BLT_PIXEL *FrameBuffer =
    (EFI_GRAPHICS_OUTPUT_BLT_PIXEL *)gGop->Mode->FrameBufferBase;

The model correctly identifies how to obtain the protocol handle, but pixel format (BGR vs. RGB byte order) requires manual verification. To verify: read the gGop->Mode->PixelFormat field and compare the PixelBitMask values against the PixelFormat enumeration.

Chinese Bitmap Fonts: AI Provides the Idea, Humans Fill the Details

Rendering Chinese text requires three steps: extracting 16×16 bitmaps from a font file, mapping them bit-by-bit to the framebuffer, and handling UTF-8 to Unicode conversion. AI can produce the bitmap-drawing framework, but encoding conversion and binary parsing must be completed by the developer. This highlights that AI still has blind spots with highly specialized low-level data formats.

Game Logic and Collision Detection

The state machine structure and rectangle-circle collision algorithms AI generates are largely correct. However, UEFI input event handling requires adaptation to the Simple Text Input Protocol, and the generated code typically needs rewriting.

Capability Assessment: Quantified Data and a Practical Checklist

Strengths (Quantified)

Metric	Performance
Project scaffolding success rate	~85%
Common algorithm first-try accuracy	~70%
Low-level protocol code requiring manual fixes	~40%

• Project initialization is fast — build configs and skeleton code are generated in one pass.
• Logical structure is clean, with good module separation and readable naming.
• Generic algorithms like collision detection are mostly correct.

Limitations

• Low-level protocol parameters and data structure alignment must be verified manually.
• Non-standard tasks like Chinese bitmap fonts and memory mapping receive only general guidance.
• Early design decisions may get overwritten after multiple iteration rounds.

Recommended Prompt Template

You are a UEFI Shell development expert. Implement [feature description] in C.
Environment: EDK2 v202405 + GCC-EFI.
Constraints: no OS dependency, manual memory management, output a .efi executable.
Provide function signatures and error handling first, then the implementation.

AI-Generated Code Review Checklist

• Do UEFI protocol parameters match the specification (e.g., GUIDs, handle types)?
• Is memory alignment satisfied for 8/16-byte requirements?
• Does character encoding conversion handle multi-byte boundaries?
• Is the framebuffer pixel format byte order verified?
• Are all error paths checked with Status return values?

Conclusion and FAQs

Core recommendation: treat AI as a “high-efficiency code draft generator,” not a fully autonomous solution. In low-level development, combining domain knowledge review with AI-generated code is essential for quality.

Frequently Asked Questions:

• Which AI model works best for UEFI coding? Prioritize general-purpose large models with strong code capabilities. Set temperature to 0.5–0.7 to balance creativity and stability.
• Can AI directly generate Chinese bitmap font code? It can provide the framework and approach, but a complete, runnable implementation requires manual encoding and parsing work.
• Can UEFI development damage my computer? There is risk. Always test in a virtual machine and back up your boot partition before making any changes.

As professional domain data continues to grow, AI performance in scenarios like this will be worth watching closely.