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An AI assistant can hand you Arduino or STM32 code that reads well, compiles cleanly, and still configures the wrong peripheral, calls a function from a different platform, or assumes a pin mapping your board does not have. The cause is structural. A microcontroller answer is only correct for one specific chip, board revision, SDK version, and physical wiring, and a language model often blends details from several of these into one confident answer.
That does not mean all model output about embedded systems is false. Published evaluations report both working embedded code and measurable failures, and the outcome depends on the model, the task, and how the model was prompted. The practical response is to pin down the exact target before you ask, and then check every device-specific claim against vendor documentation and real hardware.
What the published evidence shows
Three recent studies are useful for understanding where these errors come from. Each one is narrower than the headline it tends to inspire, so the scope of each matters.
Englhardt and coauthors (2023): exploratory tests of GPT-3.5, GPT-4 and PaLM 2
Zachary Englhardt and coauthors ran 450 experiments comparing GPT-3.5, GPT-4 and PaLM 2 on embedded systems tasks. The most cited figure from that work is that, in 50 GPT-4 trials on the study’s most complex task using a single prompt, 66% of the I2C interfaces generated were functional. That number describes one task under one prompting condition. It is not a success rate for all peripherals, all models, or all prompting styles.
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The same paper also evaluated a proposed human-AI workflow with 15 users, novice and expert programmers. The paper is an exploratory evaluation, and the models it tested are from 2023. It cannot tell you how the models you use in 2026 behave, though it does document the failure patterns that still recur in later work.
Babiuch and Smutný (2026): 27 LLMs across eight embedded scenarios
A 2026 evaluation by Marek Babiuch and Pavel Smutný tested 27 LLMs across eight embedded scenarios. Its abstract reports that hallucinated libraries or incorrect API use were the most frequent cause of compilation failure. Read this as corroboration of the pattern rather than as a precise measurement, because the abstract is the only portion whose reported findings are reflected here, and the full methods and results were not reviewed for this article.
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Grounding models in reference manuals (2025)
A 2025 University of Arizona project, described in a university record under the name Llm4mcu-Onto, explored a targeted approach. It used retrieval-augmented generation (RAG) over MCU reference manuals, fine-tuning data derived from CMSIS-SVD peripheral descriptions, and two models, GPT-4o and CodeLlama. The record reports improved extraction of peripheral details. It does not report perfect correctness, and grounding a model in documentation reduces some errors without eliminating the need to check the output against that documentation yourself.
Why microcontroller answers go wrong
A general programming question usually has one answer that holds across most environments. A microcontroller question does not. The same peripheral can be configured through different register layouts on different parts, the same HAL function can change its name or arguments between SDK releases, and the same pin label can refer to different physical balls depending on the package and board revision. Wiring matters too: an I2C example is only as good as the pull-up resistors, voltage levels and addresses on the bus it runs on.
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Language models learn from large volumes of code written for many platforms at once. When the prompt does not name the exact target, the model is likely to produce something that resembles a common pattern rather than the one that applies to your part. For instance, Arduino-style digitalWrite() calls look like an easy translation to an STM32 HAL project, but the HAL equivalent is HAL_GPIO_WritePin() and it requires a configured GPIO port and clock. The two families use different APIs even though the intent looks the same.
The failure categories to check for
The EmbedEval project documents a useful taxonomy of failure factors. Treat it as a checklist of what to look for, not as a ranking of how often each failure occurs; it is project documentation rather than an independently audited prevalence study.
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- Nonexistent or wrong APIs: functions, macros or classes that do not exist in the SDK version you are using, or that exist with different signatures.
- Cross-platform API mixing: Arduino, ESP-IDF, STM32 HAL and bare-register code combined in one answer.
- Invalid configuration symbols:
#defineflags or config options that the SDK does not recognise, or that are only valid for another family. - Initialization order: peripheral use before its clock is enabled, or before its pins are configured, such as calling
HAL_GPIO_Init()before enabling the port clock with its clock-enable macro. - Pin multiplexing: a pin assigned to a peripheral without selecting the matching alternate function, or a pin that cannot carry that function on your package.
- Version drift: code that matches a newer or older SDK release than the one installed in your project.
Compiling is not the same as working
A compiler checks that symbols exist and types match. It cannot check that a pull-up resistor is present, that a sensor is wired to the pins the code uses, that a clock setting produces the timing you expect, or that an actuator draws safe current. Code that compiles can still fail at the electrical level, and the failure may only appear under load or at a particular temperature or voltage.
Hardware-in-the-loop (HIL) evaluation addresses this gap. The Englhardt study used sensor-actuator pairs to assess generated programs against the physical world, measuring what the device did rather than whether the code built. For a hobby project, the same idea works with a logic analyser on the bus lines, a multimeter on the supply, and a known sensor or LED wired to the output you expect to drive.
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A verification workflow you can follow
Work through these steps before you flash generated firmware to a board that is connected to anything you care about.
- Name the target before asking. Give the exact part number (for example, the full STM32 order code rather than only the family name), the board revision, the framework and SDK version, the compiler toolchain, and the peripherals and external devices connected. Ask the model to state which of these it assumed.
- Check electrical facts in the device datasheet. Pin functions, alternate-function tables, supply ranges, and absolute maximum ratings belong to the exact device, so confirm each one there.
- Check register and peripheral behaviour in the reference manual. Register fields, reset values and peripheral sequences are documented here. ST, for example, separates STM32L4 documentation into distinct document types, so look in the matching one for each question.
- Check APIs and configuration symbols in the version-matched SDK documentation. Confirm that each function, header and config option exists in the release your project actually uses. Treat any function or register the model names as unverified until you find it in the documentation.
- Check the errata. Known silicon issues are published separately from the reference manual, and generated code rarely accounts for them.
- Verify the initialization sequence. Confirm clock enables, pin configuration and peripheral setup happen in the order the SDK documentation specifies.
- Compile with the real toolchain. Use the same compiler and SDK version as the project, and treat warnings about implicit declarations or unused configuration as signs of a problem.
- Test on the target. Flash the firmware to the board, observe the peripheral signals with a logic analyser or oscilloscope, and confirm the sensor or actuator responds as expected.
What each check can and cannot establish
| Check | What it can confirm | What it cannot confirm |
|---|---|---|
| Device datasheet | Pin functions, electrical limits, supply ranges for the exact part | Whether your board wires those pins as the code assumes |
| Reference manual | Register fields, reset values, documented peripheral sequences | Errata-driven deviations from documented behaviour |
| Version-matched SDK documentation | Whether an API, header or config symbol exists in your release | Whether the function behaves correctly on your hardware |
| Errata | Known silicon issues and workarounds for the revision | Issues the vendor has not yet published |
| Compilation with the real toolchain | Symbols, types and syntax match the SDK | Correct electrical behaviour, timing or system-level function |
| Test on the target or HIL setup | Observed signals and sensor or actuator outcomes | Behaviour under conditions you did not test |
Choosing a bench setup for physical checks
If you intend to test generated firmware on hardware, match the board to the project rather than to the model’s suggestion. The comparison points that matter are the MCU and board revision, whether the peripherals you need are exposed on the board, the availability of toolchain and SDK support for your framework, whether the onboard debugger can flash and inspect the chip, which pins are brought out for wiring sensors or actuators, and whether you need only a compile check or full signal-level validation. A generic microcontroller development board can serve the second purpose, but only after you confirm its MCU and software support match your project. No specific board is endorsed here.
Where LLMs still help, and where they do not
Language models are useful for drafting code, explaining what a register field means once you have read the manual, suggesting where to look in a datasheet, writing test harnesses, and reasoning through symptoms in a log. These uses keep you in the loop on the facts that matter. The places they need the most supervision are device-specific details and any behaviour that could damage hardware or harm people. None of the cited sources establishes that an LLM can certify microcontroller firmware on its own, so the human review step does not go away.
What the evidence does not establish
The available studies do not include a current, representative head-to-head benchmark across all LLMs, MCU families and toolchains. The 2023 study covers older models, the 2026 abstract is a corroborating signal rather than a full measurement, and the EmbedEval taxonomy describes failure types rather than their frequency. No single error rate applies to every model or microcontroller, so treat any claim of a universal figure with suspicion and test the specific combination you plan to use.
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