Vlad Horbach
AI Tools for Embedded Engineering
Friday, January 31, 2025
This article explores how neural network-based tools transform embedded engineering by enhancing developer productivity and simplifying complex tasks. The article presents various categories of such tools, structured according to their functionalities.
Information Search
Efficient information retrieval is essential for developers and everyday users alike, making it a top priority in this discussion. AI chat systems revolutionize information retrieval by leveraging neural networks to understand language, maintain context, and deliver structured responses. Unlike traditional search engines like Google or Bing, these tools provide a more conversational and context-aware experience.
Advantages of AI chats:
User-Friendly
AI chats allow users to ask questions naturally and receive comprehensive answers without the need for manual data sorting.Contextual Responses
These systems consider the context of prior queries, refining and simplifying conversations.Synthesis of Information
AI integrates data from various sources into a single, structured format.Personalization and Customization
Responses can be tailored in tone and complexity, suiting audiences from students to specialists.Time Efficiency
They excel at addressing complex or niche queries by providing concise summaries or direct solutions.
Disadvantages of AI chats:
Risk of Misinformation
AI systems may provide inaccurate information due to "hallucinations" or gaps in their training data.Limited Data Currency
They may lack up-to-date knowledge compared to traditional search engines.Lack of Citations
AI responses often omit direct source links, complicating fact validation.Model Dependency
The quality of responses depends on the robustness of the underlying AI model.
Leading platforms include ChatGPT (OpenAI), Claude (Anthropic), Perplexity AI, Gemini (Google), Llama (Meta), DeepSeek, and Grok (xAI). These tools continue to evolve, enhancing how users access and interact with information.
Example: ChatGPT
ChatGPT provides users with text-based features and a wide range of unique functionalities. It can create images from textual prompts, opening new opportunities for designers, marketers, and creative teams. The tool also supports working with canvases, enabling collaborative editing of documents or code, making the development and teamwork process even more convenient. Additionally, ChatGPT integrates with external tools, such as web search, allowing real-time access to current information.

Picture 1 - ChatGPT contains a variety of valuable tools.
ChatGPT can be tailored for specific tasks, such as writing code, creating graphics, or analyzing data, making it a versatile tool. This may include training the model on specialized data, adding unique vocabularies, or adjusting responses to a specific communication style. Such flexibility makes ChatGPT suitable for niche areas like technical support, educational projects, or data analysis. These instructions help refine the response, specifying which aspects should be included or excluded. This dramatically improves the accuracy and relevance of results, especially for complex or multi-task queries.

Picture 2 - Instructions will help avoid entering the common prompt repeatedly.
OpenAI has introduced a beta feature in ChatGPT called "Work with Apps", which enables the AI to scan the contents of a user's screen and provide real-time suggestions. By analyzing on-screen content, including code and application interfaces, ChatGPT can offer context-aware recommendations, such as code optimizations, bug fixes, or workflow improvements. For example, while working in Xcode, ChatGPT can suggest enhancements for slow-performing functions or identify potential errors. However, as this feature is still in beta, it is currently limited to ChatGPT Plus users on Mac and may not fully support complex or non-standard interfaces.
It assists developers in writing, debugging, and optimizing code. Those who want to explore these capabilities professionally can take a course at “Learn Prompting,” where they can learn and earn a certificate validating their skills with this advanced tool.
Conclusion
AI chats offer faster, more convenient, and context-aware access to information. However, they require users to approach them critically and verify facts, as they do not always guarantee transparency or complete accuracy. Nevertheless, this technology is rapidly evolving, and the quality of the information provided continues to improve.
Writing Code
Embedded software development traditionally requires deep platform knowledge and manual coding in low-level languages like C and C++. This process is labor-intensive and requires careful attention to memory and performance optimization, which often leads to errors and significant time investments.
The advent of language models (LLMs) is transforming this landscape. Models trained on vast amounts of code can generate snippets, functions, and even modules based on textual descriptions. They simplify the creation of complex algorithms and reduce development time through intelligent suggestions and automated validation, enhancing code quality.
Example: GitHub Copilot
GitHub Copilot is an advanced tool for code automation. It is compatible with many popular development environments, such as Visual Studio Code, JetBrains, Xcode, and others.
Its advantages include a user-friendly interface, intelligent suggestions, and hands-on learning opportunities. Users can selectively give the assistant access to project files, greatly simplifying interaction and reducing routine actions such as copying and pasting text into a chat. Additionally, users can choose a language model for their work: ChatGPT 4.0 or Claude 3.5.
The service is free, but a paid subscription offers enhanced features such as custom settings, improved autocomplete accuracy, and integration with enterprise repositories. Thus, it is beneficial for both beginners and experienced professionals.
Microsoft also offers a course on the Microsoft Learn platform. Upon completing the course, participants receive a certificate validating their knowledge and skills in using GitHub Copilot. This certification can be valuable in professional activities, showcasing your expertise in leveraging modern development tools.
Tool Features
Long-standing tools can also handle the features discussed below. Still, the assistant outperforms them by generating new ideas, providing broader recommendations, and taking into account the nuances of a specific project. Unlike static code analyzers and linters, which mainly check compliance with specific rules and patterns, this kind of assistant aims to “understand” the task context in a broader sense, propose different solution options, and explain the logic behind its approach. Nevertheless, traditional tools still offer benefits in narrowly focused tasks - they strictly ensure the code complies with set criteria, produces minimal false positives, and requires fewer resources.
Code Autocompletion
Copilot suggests the most likely ways to complete a line of code based on the context of the current project. For instance, the assistant automatically provides syntactically correct suggestions when writing loops or conditional statements. This speeds up the workflow and helps avoid minor errors, such as missing parentheses or incorrect function calls.

Picture 3 - The assistant completes a line based on the preceding content.
Function Generation
One of the most impressive features is the ability to create entire functions based on textual descriptions. For example, simply writing that a function is needed to calculate the sum of array elements will prompt Copilot to generate an appropriate implementation. This is especially useful for quickly prototyping or handling routine tasks.

Picture 4 - GitHub Copilot can even suggest ready-made functions based on the context.
Writing Comments
The assistant aids in writing detailed and clear comments for the code. It can automatically describe a function’s input and output parameters, explain complex algorithms, or highlight why specific constructs are used. This significantly simplifies collaborative development and code maintenance.
Error Correction
Copilot can detect errors, such as incorrect variable usage or incompatible data types. Upon identifying an issue, it suggests fixes, including performance improvements or stylistic adjustments. This reduces the likelihood of critical errors.

Picture 5 - The Assistant handled an additional case, added comments, and improved the style.
Code Optimization
The assistant can analyze existing code to suggest more efficient implementations. For instance, it may replace nested loops with more optimal algorithms or recommend using standard libraries instead of custom solutions. This is particularly relevant for embedded systems, where conserving memory and processing time is crucial.

Picture 6 - Code before editing.

Picture 7 - Suggestions from the assistant for improvement.
Educational Hints
Copilot is also helpful for training developers. It offers ready-made solutions and explains them, such as how a specific algorithm works or why a particular approach was chosen. This makes it an excellent tool for those looking to deepen their programming knowledge. The following section provides more details.
Personal experience (from the author)
I want to share my experience using AI tools in a real-world project in this section. The task was to develop a desktop application for log parsing with a graphical user interface (GUI) and data visualization on charts. This project was both challenging and rewarding, as it required a systematic approach and the integration of various AI tools to achieve the desired outcome.
The project was divided into several stages, each addressing a specific aspect of the development process. From selecting the right tools to implementing the GUI and handling exceptions, AI tools like ChatGPT and GitHub Copilot played a crucial role in accelerating the development and ensuring the quality of the final product. Despite having no prior experience with Python and the libraries used, the project was completed within a few working days, thanks to the combination of formal education, search engines, and AI tools.
This experience demonstrated the practical benefits of AI tools in embedded engineering and highlighted their potential to empower developers to tackle complex tasks more efficiently. Below, I will walk you through the stages of the project and how AI tools contributed to its success:
Choosing Tools
The initial task was to identify the tools suitable for the project implementation. Information on modern libraries for data processing and chart visualization was requested in a chat. Among the suggested options, the following stood out: “PyQt6” for creating the graphical user interface and “matplotlib” for plotting charts.
Python was chosen because it is accessible and straightforward compared to other options. An additional advantage was the ability to complete the project without cost. Also, I hadn't studied Python before, which added an extra layer of interest to the task.Creating a Console Application for Log Parsing
At this stage, the focus was on developing a basic log parser. Using the file upload feature in ChatGPT, an example log and the required processing logic were provided. The generated code structured the data into classes and implemented basic parsing operations.
After corrections and refinements, the code successfully processed the data. Additional validation was done using other language models (Claude 3.5) and PEP8 recommendations.Adding a Graphical User Interface
The project was divided into modules to implement the graphical user interface. The necessary functionality was described, and ChatGPT proposed a working template adapted to the requirements. During development, styles and user-friendly labels were added to improve the application’s appearance.Plotting Charts
The initial implementation included a single chart. Gradually, support for additional visualizations was added. Some charts required complex customization, such as adjusting axes or applying unique styles. We studied the Matplotlib documentation to address these challenges and searched for solutions on specialized forums.Exception Handling
The final stage involved testing the application under various scenarios. ChatGPT and Copilot assisted in implementing basic error handling, such as checks for empty files or invalid paths for saving data. More complex aspects related to log processing were manually refined, including support for specific scenarios and improving the application's robustness.
As a result, a fully functional application for log processing and data visualization was created. Despite having no experience with Python and the libraries used, the project was completed within a few working days. The success was made possible by combining formal education, search engines, and AI tools such as ChatGPT and GitHub Copilot.

Picture 8 - A program is written using AI tools.

Picture 9 - An interactive measurement chart is displayed for each device in the log.
Competitors and Alternatives to GitHub Copilot
GitHub Copilot is a popular code autocompletion tool, but there are various alternatives available on the market:
Codeium
A free autocompletion tool for more than 20 programming languages (e.g., JavaScript and Python), it processes code locally, enhancing privacy. Its support for a wide range of languages and local data processing makes it appealing to confidentiality-conscious users. However, its integration capabilities and overall functionality may fall short compared to competitors.Cody AI
Cody integrates with SourceGraph to navigate large codebases and is excellent for code analysis and working on extensive projects. It supports integrating various large language models (LLMs) from providers such as Anthropic and OpenAI. Additionally, it allows local models through Ollama, offering flexibility in choosing the right model for your tasks. However, its functionality depends on a paid subscription, and language support may be limited.Tabnine
It supports more than 30 programming languages and integrates with popular IDEs. Its versatility and compatibility with over 15 IDEs make it stand out among similar tools, and GPT-based models ensure high-quality autocompletion. However, some features are only available in the paid version, and cloud-based processing may raise privacy concerns.Cursor AI Code Editor
Cursor is another powerful AI-driven code editor built on Visual Studio Code, offering advanced features such as intelligent code completion, context-aware suggestions, and natural language-based code generation. It supports multiple AI models, including GPT-4 and Claude 3.5, making it highly adaptable to different programming tasks. Cursor excels in understanding large codebases, providing developers with tools to navigate, refactor, and document code more efficiently. While it shares similarities with GitHub Copilot, Cursor’s deep integration with project context and support for multi-file analysis make it a strong alternative for teams working on complex embedded systems.
Limitations
The application of Large Language Models in embedded systems development presents several challenges that must be addressed to ensure reliability and security.
Code Validation
While LLMs can generate syntactically correct code, they often fail to account for hardware-specific requirements and resource constraints. This necessitates rigorous testing and verification, particularly in mission-critical systems where errors can have severe consequences.Security Risks
Generated code may inherit vulnerabilities if the model is trained on insecure datasets. Furthermore, reliance on cloud-based processing introduces risks of data leakage, especially when handling sensitive or proprietary information. A potential mitigation strategy is local models, which process data on-device, reducing exposure to external threats. However, this approach requires additional security measures, such as data encryption and strict access controls.Contextual Understanding
LLMs may not fully comprehend the technical nuances of embedded systems, leading to suboptimal or incorrect solutions. This limitation underscores the need for human oversight and domain-specific fine-tuning of models.
Testing
Integrating language models into software testing improves efficiency significantly and introduces new error detection and diagnosis methodologies. Below, we outline LLMs' key applications and limitations in this domain.
Automating Routine Tasks
Software testing often involves repetitive and time-consuming tasks, such as generating test cases and validating outputs. LLMs can automate these processes by creating test scenarios based on technical specifications or requirements. This reduces the manual effort required, allowing developers to focus on higher-level strategic aspects of testing.Generating Test Data
LLMs excel at synthesizing diverse datasets, including edge cases and rare scenarios, critical for comprehensive test coverage. This capability is particularly valuable for testing systems that process varied input formats, such as communication protocols, sensor data, or user commands.Identifying Vulnerabilities
Traditional testing methods may overlook specific attack vectors or non-standard system behaviors. LLMs can analyze known vulnerabilities and propose novel testing scenarios, enhancing the detection of issues such as incorrect data handling or faulty interactions with external devices.Simplifying Documentation and Feedback
Documenting test results and generating reports are essential but labor-intensive tasks. LLMs can automate the creation of structured reports, highlighting key issues and suggesting solutions. Additionally, they can assist in producing training materials and instructions, facilitating better collaboration within teams.
Limitations and Challenges
While LLMs offer substantial benefits, their application in software testing is not without challenges:
Task Misinterpretation:
LLMs may misinterpret testing requirements, leading to incorrect or irrelevant results. Clear task definitions and proper configuration are essential to mitigate this risk. For instance, ambiguous or incomplete instructions may result in test cases that fail to align with the intended objectives.Contextual Understanding:
LLMs often fail to fully grasp the technical nuances of complex or highly specialized systems, such as embedded systems, real-time control systems, or systems with non-standard protocols.Resource Requirements:
Training and adapting LLMs for specific testing scenarios may demand significant computational resources and expertise.Security Risks:
LLMs could unintentionally expose sensitive information when generating test cases or analyzing data, especially if trained on confidential or proprietary datasets.
Data Processing
Language models offer transformative capabilities for processing the extensive data generated by embedded systems, including logs, telemetry, and test results. Here are the key functionalities they enable.
Log processing
Convert unstructured logs into organized formats such as JSON or tables for easy analysis.
Extract critical data points like timestamps and keywords to highlight significant events.
Handle log images by integrating OCR (Optical Character Recognition) for text extraction.
Transforming data from documentation
Quickly transform hardware documentation into usable code, such as enums, macros, or protocol command structures.
Automate mapping of register values to respective functions, reducing the chance of manual errors.

Picture 10 - Automatic data conversion into code.
Data Visualization
Generate scripts for charting telemetry or system performance using tools like matplotlib.
Automate formatting of raw telemetry data for graphical representation.
Provide trend analysis and highlight potential issues directly from visualized data.

Picture 11 - An error (incorrectly closed brackets) was deliberately added to the end of the file. AI recognized and processed this.
Statistical Processing
Generate scripts for key metrics like distributions, medians, and standard deviations.
Recognize patterns and trends to assist in optimizing system performance.
Use probabilistic methods, such as Bayesian analysis, to provide actionable insights.

Picture 12 - Calculation based on data from the previous example.
Documentation
The role of an engineer has long expanded beyond writing code and configuring hardware. Today, professionals are often required to create clear documentation, prepare reports, and present the results of their work. However, these tasks can be time-consuming. Language models can reduce the effort spent on writing and improve the quality of the output.
Creating high-quality documentation involves four key stages:
Information Gathering: Engineers collect relevant data about the device, including its functions and features.
Structuring Information: Language models help organize the data by proposing logical sections, such as "Description," "Setup Instructions," and "Troubleshooting."
Text Generation: Engineers can use prompts with technical details to guide the model in generating a foundational document draft.
Editing: The model assists in refining the text, improving style, correcting grammatical errors, and tailoring the content to the target audience.
This approach significantly reduces the time needed to prepare documentation, allowing engineers to focus on development's core aspects.
Canvas
One of ChatGPT’s unique features is its canvas - a tool that makes working with text even more convenient. Key advantages include:
Real-Time Editing: Users can make immediate adjustments, add comments, and correct errors seamlessly.
Information Structuring: Text can be organized into logical blocks with hierarchical headings, improving navigation and readability.
Dynamic Data Updates: The canvas eliminates the need to scroll through lengthy chats, as information is updated directly within the workspace, replacing outdated content
These capabilities make the canvas an essential tool for managing complex projects that demand precise content control and efficient collaboration.

Picture 13 - Canvas is ideal for working with long-form content, enabling iterative edits, leaving comments, and collaborating efficiently.
Language Practice
IT professionals frequently encounter tasks that demand precise wording and confident communication. Key examples include:
Writing Reports and Documentation
The clarity and accuracy of documentation directly impact how well colleagues or clients understand a project.International Collaboration
Working with global teams requires effective communication in English or another shared language.Presentations and Public Speaking
Conveying complex technical concepts in an accessible manner is a critical skill.Meetings and Negotiations
Successful communication requires using appropriate terminology, interpreting technical jargon, and translating engineering ideas into business terms.
For IT professionals working abroad or collaborating with international teams, common challenges include:
Limited confidence in spontaneous spoken communication.
Errors in written communication may undermine professionalism.
Accent and pronunciation issues that hinder clarity.
Difficulty adapting technical language for non-technical audiences.
Leveraging ChatGPT for Language Practice
ChatGPT offers a transformative approach to language practice, addressing these challenges through the following features:
Conversational Practice: Engage in dialogues in multiple languages, receive instant feedback, and request corrections or more natural phrasing.
Pronunciation Support: While ChatGPT cannot evaluate speech directly, it can generate texts with pronunciation guides or provide region-specific phrases (e.g., British or American English).
Customizable Scenarios: Tailor practice sessions to specific needs, such as business English, technical terminology, or casual expressions. For instance, simulate a technical meeting or a client conversation.
Advantages of AI-driven language practice:
Instant Feedback
Receive corrections and suggestions immediately, without waiting for a teacher or colleague.Stress-Free Environment
Interact with AI without the fear of embarrassment, fostering confidence.Flexibility
Practice anytime and anywhere—at home, work, or on the go.Affordability
ChatGPT is accessible on multiple devices, including smartphones. While the free version has daily limits, the ChatGPT Plus subscription is significantly more cost-effective than hiring a tutor.
By integrating ChatGPT into their workflow, IT professionals can enhance their language skills, improve communication efficiency, and overcome barriers to international collaboration.
Conclusion
Integrating AI tools, particularly neural network-based language models, into embedded engineering has significantly transformed the landscape of software development, testing, data processing, and documentation. These tools, such as ChatGPT, GitHub Copilot, and others, offer various functionalities that enhance developer productivity, reduce errors, and streamline routine tasks. AI tools are invaluable for engineers, from automating code generation and optimizing algorithms to simplifying documentation and improving language skills.
However, while the benefits are substantial, it is crucial to approach these tools with a critical mindset. The risk of misinformation, code vulnerabilities, and the need for thorough validation remain significant challenges. Engineers must ensure that the generated code and documentation meet the specific requirements of their projects, especially in mission-critical systems where reliability and security are paramount.
Moreover, the rapid evolution of AI technology means that these tools are continually improving, offering even more significant potential for innovation and efficiency in the future. By effectively leveraging AI tools, engineers can focus more on complex problem-solving and creative development aspects, ultimately driving progress in embedded systems.
In conclusion, AI tools are not just a convenience but a necessity in modern embedded engineering, provided they are used responsibly and their limitations are clearly understood. As technology advances, the synergy between human expertise and AI capabilities will undoubtedly lead to even more groundbreaking developments in the industry.