Using AI in driver development

Overview

As technology advances rapidly and software quality requirements grow, development automation is becoming a key competitive factor. Using artificial intelligence (AI) in driver development can significantly reduce development time, minimize errors, and improve the quality of the final product.

OEM documentation contains:

• Specifications and technical requirements;
• Operating algorithms of metering devices;
• Descriptions of interaction protocols.

This is an extensive (but not always exhaustive) manual that includes:

• Hardware interfaces and communication protocols — a detailed description of the data exchange sequence;
• Signal processing algorithms — specifications for collecting, filtering, and interpreting measured parameters.

However, the large volume and complexity of such documentation, as well as its incompleteness or low quality, often create obstacles to quickly creating new drivers. This is where artificial intelligence comes to the rescue, since it can analyze and structure large volumes of technical data.

Benefits of using AI in driver development

Using AI to assist with manufacturer documentation provides the following benefits:

• Reduced development time. AI can cut the time it takes to create a new driver in half. The technology helps compress information from documentation, extract relevant data on demand, and generate code snippets (for example, driver methods).
• Fewer errors. AI helps identify and correct common errors in documentation (for example, inaccuracies in protocol descriptions). This reduces the risk of bugs in the finished driver.

Algorithm for using AI

The following algorithm has emerged during AI-assisted development:

Data preparation

Documentation is converted to vector format (using embedding models such as text-embedding-ada-002 from OpenAI) and stored in a vector database.

Semantic search

The user formulates a query (for example: “Provide a description of the clock archive protocol and the algorithm for reading it”). The query is converted into a vector, after which the database is searched. The relevant fragments found are passed to the analysis model (for example, Deepseek R1) along with the text prompt.

Code generation

Based on the received context, the AI generates basic driver methods in the selected programming language (in this case, Golang).

It is important to note that AI does not replace an understanding of device protocols, but it speeds up access to information and simplifies its interpretation, thereby optimizing the development process.