AI for Engineers: LLMs in Firmware and Model-Based Engineering

Artificial intelligence is rapidly reshaping engineering workflows — not as a replacement for human expertise, but as a powerful accelerator for tasks that once consumed large portions of development time. Two areas seeing major practical benefits are embedded/firmware development and model-based engineering (MBE).

Although these disciplines focus on different levels of abstraction, they share a common pattern:
AI assists with structure, scaffolding, exploration, and consistency — while engineers retain responsibility for correctness, constraints, and design intent.

AI for Firmware Developers: Why Claude-Style Coding Assistants Are Actually Useful

Embedded developers have long believed that AI tools “don’t understand the hardware,” and historically that was true — traditional autocomplete systems were easily confused by low-level code, real-time constraints, memory limits, or board-specific dependencies.

However, modern LLM-powered coding assistants (such as those referred to as Claude Code) behave very differently from traditional autocomplete tools.
Their strengths align surprisingly well with the needs of embedded teams:

1. Large-Context Understanding

Newer LLMs can take in extremely large windows of context. Instead of guessing from a few lines of code, they can analyze:

• Entire driver files
• Hardware abstraction layers
• Register maps
• Build configurations
• Coding standards

This allows AI to propose code that aligns with the architectural patterns already in place.

2. Fast Generation of Structured Boilerplate

Firmware often requires repetitive scaffolding:

• Peripheral initialization
• RTOS task structures
• Interrupt handlers
• State machines
• Communication protocol frames
• Test harnesses and sanity-check routines

AI excels at producing structured templates for these elements, allowing engineers to tune and refine rather than hand-craft every file from scratch.

3. Accelerated Reverse Engineering of Legacy Code

Many firmware teams inherit old codebases with:

• Sparse or outdated documentation
• Inconsistent coding styles
• Tribal knowledge that disappears with turnover

AI can summarize functions, infer intent, highlight suspicious patterns, and create test cases around undocumented modules — giving engineers a head start on understanding systems they did not build.

4. Embedded Constraints Still Require Human Judgment

AI suggestions must be reviewed carefully to ensure:

• Timing requirements are met
• Stack and memory budgets are not exceeded
• Interrupt latency is preserved
• Power-saving constraints are respected
• Hardware errata workarounds remain intact

This is why AI becomes a partner in firmware development, not an automatic code generator. It handles the drafting and exploration; developers handle correctness and optimization.

5. How Claude is different

Unlike many code assistants that focus on short, autocomplete-style predictions, Claude distinguishes itself through its ability to reason across extremely large context windows and maintain coherence over complex, multi-file interactions. This makes it uniquely valuable for embedded and systems developers who often work with intricate codebases, hardware documentation, and tightly coupled architectural patterns. Claude does not simply guess the next line — it analyzes structure, intent, constraints, and previously defined behaviors, producing suggestions that align with the overall design rather than isolated snippets. Its conversational nature also allows engineers to interrogate decisions, request explanations, and iteratively refine solutions in a way that mimics a highly skilled peer reviewer. In practice, this leads to higher-quality drafts, faster comprehension of legacy systems, and significantly reduced friction during early development and exploration, setting Claude apart from traditional predictive code assistants.