AI Coding Assistants in UEFI Bare-Metal Development: A Hands-On Practice and Performance Evaluation

1. Introduction: Why UEFI Bare-Metal Is a Demanding Testbed for AI Coding

With AI-assisted programming tools proliferating rapidly, an open question has emerged: how can we objectively measure their real-world capability? Unlike typical web or application-layer development, the UEFI (Unified Extensible Firmware Interface) environment represents an extreme scenario—there is no operating system, no standard libraries, and no graphical framework. Developers must build everything from the ground up, starting at the hardware abstraction layer.

This article documents a complete hands-on project: building a graphical Breakout game with Chinese character support that runs directly in the UEFI Shell, using only basic compilation tools on Windows and an AI coding assistant as the sole code generator. We analyze the AI’s performance across five dimensions: autonomous planning, code correctness, iteration efficiency, cost controllability, and adaptability to specialized technical challenges.

2. Experimental Design: A Fair Evaluation Under Strict Constraints

2.1 Constraints

To ensure objectivity, the experiment was bound by rigorous conditions:

• Tool restrictions: Only system-preinstalled C/C++ compilers and build toolchains were permitted. No third-party libraries, frameworks, or auxiliary tools were introduced.
• Zero-code starting point: The local repository began empty. The human developer wrote no code manually; everything was AI-generated.
• Minimal human guidance: The human provided only the task objective and necessary environment details, without participating in implementation design or debugging.
• Clear deliverable: A Breakout game executable in the UEFI Shell, featuring graphics rendering, user interaction, collision detection, a scoring system, and Chinese character display.

2.2 Why Breakout

Breakout is a classic project in computer science education, covering core game development concepts: main loop design, input handling, 2D collision detection, sprite rendering, and state management. Porting it to UEFI increases complexity exponentially because:

• There are no OS-level abstractions for file I/O, memory management, or thread scheduling.
• Graphics output depends on the UEFI GOP (Graphics Output Protocol), requiring manual framebuffer management.
• Chinese text rendering must be implemented within extremely limited resources, typically via custom bitmap fonts or simple encoded glyph schemes.

This choice gives the experiment sufficient technical depth to thoroughly test the AI assistant’s comprehensive programming ability.

3. Full Development Process Review

3.1 Round 1: From Zero to a Working Prototype

3.1.1 Typical Early-Stage Errors

The AI made two representative mistakes during project initialization, exposing its “blind spots” when facing unfamiliar domains.

Mistake 1: Blind copying of EDK2 source code. The AI attempted to copy portions of an open-source UEFI framework’s source as a starting point, but environment differences and version incompatibilities caused compilation failures. This shows that without clear context, the AI tends to replicate existing patterns but lacks precision in environment adaptation.

Mistake 2: Confused build-system commands. During the build process, the AI repeatedly mixed up invocation methods across different build systems, generating incompatible compilation commands. Several rounds of dialogue correction were needed before the correct build flow was established.

3.1.2 First Successful Run

After several iterations, the AI produced a minimal viable version: a basic framework that, when launched in the UEFI Shell, displayed simple rectangles and a paddle. This milestone confirmed that the AI can understand fundamental UEFI protocols, write entry-point functions, and configure GOP mode—but code quality still had significant room for improvement.

3.2 Round 2: Deep Iteration from Functional to Polished

3.2.1 Feature Enhancement

On top of the basic framework, the AI progressively implemented:

• Ball movement and boundary collision: Velocity vector calculations and four-wall bounce logic.
• Brick layout and elimination detection: A multi-row brick array with rectangle-based ball-brick collision detection.
• Scoring and life system: Score accumulation and life value management.
• Keyboard input response: Capturing key events via UEFI’s Simple Input Protocol to drive paddle movement.

Code generation was fast in this phase, but some modules had logical flaws—for instance, collision detection allowed ball tunneling at high speeds, requiring fine-grained step decomposition to fix.

3.2.2 Visual Optimization

To improve the user experience, the AI conducted multiple visual refinements:

• Added color differentiation for bricks, ball, and paddle.
• Introduced simple animations (e.g., a flash effect on brick elimination).
• Optimized framebuffer refresh strategy to reduce screen tearing.

3.2.3 Chinese Character Rendering: The Biggest Challenge

Displaying Chinese characters was the most technically demanding part of the project. In the UEFI environment, there is no off-the-shelf Chinese font rendering engine. The AI had to choose and implement one of the following approaches:

1. Pre-rendered bitmap font scheme: Commonly used Chinese characters are pre-rendered as bitmaps and drawn via lookup tables at runtime.
2. Compact encoding scheme: Supporting a limited character set with compressed encoding to minimize storage.

The AI ultimately chose the bitmap font scheme but encountered encoding compatibility issues—generated Chinese comments triggered character-set warnings during compilation, causing failures on some platforms. After adjusting the source file encoding declarations and standardizing on a universal character encoding, the issue was resolved.

This episode reveals a critical lesson: AI often lacks fine-grained understanding of compiler behavior regarding cross-platform character encoding and requires explicit encoding guidelines from humans.

3.2.4 Final Delivery

After multiple debugging rounds, the AI delivered a fully functional Breakout game with Chinese menus and on-screen prompts. The game can be copied to a USB drive and run directly from the UEFI Shell boot menu on any UEFI-compatible machine, with no preinstalled environment needed.

4. Summary of Tasks Completed Autonomously by AI

Across the entire process, the AI independently handled the following core tasks:

Task Category	Specifics
Project architecture	UEFI application entry point, module division, build process
Protocol integration	GOP graphics output, Simple Input Protocol, Shell parameter parsing
Game logic	Ball physics, collision detection, state machine management
Graphics rendering	Framebuffer operations, color filling, simple animations
Chinese support	Glyph extraction, encoding conversion, display adaptation
Debugging and fixes	Compilation error troubleshooting, logic defect correction, performance tuning

Human involvement was mainly limited to defining requirement boundaries, correcting directional errors, and providing encoding规范 guidance.

5. Cost and Performance Analysis

5.1 Cost Considerations

Using a high-capability AI coding model (such as a large language model with strong reasoning) for this type of development, under current industry pay-per-use pricing, a medium-complexity project may cost anywhere from tens to hundreds of currency units. For one-off validation or educational demonstrations, this is acceptable. For large-scale daily development, however, model selection matters—with sufficient prompt engineering, mid-tier models can often accomplish similar tasks at lower cost.

As commonly observed in the industry: top-tier models are undeniably powerful, but for most practical development scenarios, choosing a “good enough and economical” option is the more pragmatic approach.

5.2 Performance Assessment

• Strengths: Rapid generation of skeleton code, cross-domain knowledge integration (e.g., understanding both UEFI protocols and game logic simultaneously), and self-correction across multiple iteration rounds.
• Limitations: Insufficient depth in extreme low-level details, tendency to produce code that “looks correct but is wrong” in unfamiliar domains, and lack of awareness of subtle compilation environment differences.

6. Conclusions and Recommendations

This practice demonstrates that AI coding assistants already possess the basic capability to build runnable programs from scratch in extreme scenarios like UEFI bare-metal development—but their performance is highly dependent on prompt precision and human oversight at critical decision points.

For developers and organizations, we recommend the following strategies:

1. Define clear boundaries: When using AI-assisted coding, specify environment constraints and acceptance criteria explicitly to prevent the AI from wasting resources on uncertain directions.
2. Layered usage: Position AI as an “efficient executor” rather than an “architecture decision-maker,” and retain human judgment at key design nodes.
3. Cost optimization: Select a model with capability appropriate to task complexity—avoid over-provisioning.
4. Prioritize testing: AI-generated code must undergo rigorous compilation verification and runtime testing, especially in low-level development where a single subtle error can crash the entire system.

AI-powered coding tools are reshaping development workflows, but they are not a silver bullet. Understanding their capability boundaries, leveraging their efficiency advantages, and mitigating their potential risks are the core skills developers should master today. Platforms such as Baidu AI Cloud are continuously lowering the barrier to accessing such intelligent programming capabilities, providing infrastructure support for automated exploration across a broader range of technical scenarios.