Nordic Semiconductor adds AI-assisted development tools to nRF Connect SDK and nRF Cloud

Nordic Semiconductor added AI assistance to its nRF Connect SDK, training it on SDK docs and nRF Cloud data rather than using generic models. The tool speeds prototyping but still makes mistakes and requires developer oversight.

Categorized in: AI News IT and Development
Published on: Jun 03, 2026
Nordic Semiconductor adds AI-assisted development tools to nRF Connect SDK and nRF Cloud

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Nordic Semiconductor integrated AI-assisted development into its nRF Connect SDK and nRF Cloud platform, enabling workflows that span from initial prototype to deployed fleet management.

The solution differs from generic large language models. Nordic trained its AI specifically on nRF Connect SDK documentation and nRF Cloud data, then integrated it with developer IDEs. The approach reduces token costs by connecting to generative code tools like Claude Code, Cursor, and GitHub Copilot through a specialized model.

How It Works

The system uses an implementation of the MCP (Model Context Protocol), where Nordic's MCP servers give AI assistants access to validated sources. These include SDK documentation, API references, device configurations, and field data from nRF Cloud.

The AI agent handles tedious tasks, speeds prototyping, and aids debugging. Practical use cases include SDK migrations, custom board bring-up, and diagnosing crashes on deployed devices.

What It Does Well-and Doesn't

Nordic published video examples covering AI-assisted migration, fleet diagnostics, DeviceTree and Kconfig generation, release validation, and shell command development.

Code review remains essential. In one video example, the AI agent added random peripherals-a button and extra LEDs-that required manual correction. The tool assumes the developer can write specific, technical prompts; vague requests won't produce usable results.

Think of AI agents as interns or junior engineers who need supervision. They make mistakes and require oversight before code reaches production.

The Broader Question

AI adoption in software engineering raises concerns about job displacement. Some companies fired engineers citing AI efficiency, while others rehired after discovering prohibitive AI costs. Some analysts suggest cheaper development tools could increase demand for software engineers rather than reduce it.

For now, the evidence points to AI as a tool that augments developer productivity, not replaces it.


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