Software development
AI-assisted implementation, exploration and development support grounded in hands-on software engineering.
AI acceleration with engineering judgement
AI-Assisted Software Engineering
Use AI to accelerate delivery while maintaining engineering quality and critical review.
I apply AI-assisted tools to software development, solution design, technical analysis, architecture documentation, code review and engineering productivity.
Engineering contextReviewed output
Tools supporting, not replacing, engineering
What AI-Assisted Engineering Means
AI-assisted software engineering uses tools such as ChatGPT and OpenAI Codex to support analysis, design, implementation and documentation tasks.
The value comes from combining faster exploration and production with software engineering experience, clear context and critical evaluation of every important output.
AI can help investigate options, draft artefacts and review implementation details, but architecture decisions and delivered software still require professional judgement and accountability.
Support across the engineering lifecycle
How I Use AI in Engineering
Solution design
Exploration of technical options and support for clear, practical solution proposals.
Technical analysis
Faster investigation and organisation of complex technical information while keeping sources and assumptions under review.
Architecture documentation
Assistance with structured architecture documentation, sequence diagrams and implementation proposals.
API specifications
Support for drafting and refining API documentation that engineering teams can understand and use.
Code review
AI-supported code review combined with technical experience and critical human validation.
A controlled path from prompt to delivery
How I Work With AI
Useful AI output begins with a well-understood problem and ends with deliberate engineering review.
- 01
Understand the objective
Clarify the business need, technical context, constraints and expected result.
- 02
Prepare relevant context
Provide the information and boundaries needed for a focused engineering task.
- 03
Explore options
Use AI to accelerate research, comparisons, drafts and alternative approaches.
- 04
Challenge the output
Check assumptions, gaps, feasibility and alignment with the actual environment.
- 05
Refine the engineering result
Turn useful material into maintainable code, documentation or a practical design.
- 06
Validate quality
Review the result critically before it informs a decision or enters delivery.
AI supported by practical technical depth
Engineering Foundation
I combine AI-assisted workflows with more than 15 years of experience across software development, IT analysis, solution architecture, integrations and enterprise environments.
Hands-on engineering experience helps me assess whether generated designs and implementation proposals are realistic, understandable and maintainable.
Technical background
- PHP, Laravel and Livewire
- Python, Go and JavaScript
- SQL, MySQL and PostgreSQL
- REST APIs, SOAP and integration patterns
- Event-driven architecture and microservices
- UML, BPMN, ArchiMate and PlantUML
Acceleration without abandoning responsibility
Working Principles
- Start with the business and engineering problem, not the tool.
- Use AI output as material for evaluation, not automatic truth.
- Keep architecture decisions understandable and documented.
- Review generated code and specifications critically.
- Maintain engineering quality while improving productivity.
- Choose tools according to the task and project context.
Applied in day-to-day technical work
Relevant Experience
Daily engineering practice
AI-Augmented Engineering
Software development, analysis and architectureDaily use of ChatGPT, OpenAI Codex and AI-assisted engineering tools for development, solution design, technical analysis, architecture documentation, code review and engineering productivity.
Architecture documentation
AI-Assisted Technical Artefacts
Diagrams, specifications and proposalsAI-assisted generation of technical documentation, sequence diagrams, API specifications and implementation proposals.
09/2024–03/2025
Solution Architect — Pre-sales
xITee k.s.Use of ChatGPT, DeepL and DALL-E to improve efficiency and decision support during tender concepts and solution proposals, plus architectural and business support for integrating AI solutions.
Start with a concrete engineering task
Let’s discuss where AI can help.
Share the development, analysis, design or documentation workflow you want to improve. We can identify where AI assistance is useful and where engineering judgement must remain central.
MartinModl.cz