OneRange

AI for Engineering & Software Development

This course helps software engineers accelerate development with AI coding assistants while keeping code secure and reviewed. Learners use AI for coding, review and debugging, tests and documentation, and apply secure-use practices. They leave shipping higher-quality code faster.

What this course covers

The course runs across 15 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

  • Introduction to AI Coding Assistants — Understanding the landscape of LLM-based coding tools, prompt engineering for developers, and setting up the IDE environment.
  • Code Generation and Autocomplete — Leveraging inline suggestions and chat interfaces to generate boilerplate, implement algorithms, and accelerate feature development.
  • Refactoring Existing Code with AI — Using AI assistants to restructure legacy code, improve readability, and apply modern design patterns safely.
  • AI-Assisted Code Reviews — Employing AI to analyze pull requests, identify potential logical flaws, and ensure adherence to team style guides.
  • Debugging and Error Resolution — Diagnosing stack traces, runtime errors, and compiler warnings rapidly using AI-guided troubleshooting.
  • Automated Unit Test Generation — Writing comprehensive unit tests, mock objects, and edge cases using AI prompts to increase test coverage.
  • Generating Documentation and Comments — Using AI to generate clear docstrings, API documentation, and README files directly from source code.
  • Identifying Security Vulnerabilities with AI — Using AI tools to scan code for common security flaws, OWASP Top 10 vulnerabilities, and hardcoded secrets.
  • Secure AI Use and Data Privacy — Understanding IP exposure risks, licensing concerns, and how to configure AI tools to prevent leaking sensitive codebase data.
  • Mitigating AI Hallucinations in Code — Techniques for verifying AI-generated APIs, verifying non-existent library dependencies, and maintaining critical oversight.
  • Optimizing Performance and Complexity — Using AI to analyze the time and space complexity of algorithms and suggest optimized alternatives.
  • Integrating AI into CI/CD Pipelines — Exploring automated AI-driven checks, static analysis, and code quality gates within continuous integration workflows.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Secure Coding Practices, Code Generation, Peer Review, Working with AI Tools.

  • Secure Coding Practices
  • Code Generation
  • Peer Review
  • Working with AI Tools

Who it is for

Software engineers, dev teams. The material is pitched at intermediate level, and takes roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Function: Engineering track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

See how this training works for the whole function on AI training for Engineering.

Also in this track

  • AI for Security Engineering — A course for security engineers on AI in defensive work: log analysis, detection, incident response, and secure code review - plus the AI-specific threats they now defend against.
  • Advanced AI-Assisted & Agentic Development — An advanced course on using agentic coding tools and AI across the whole SDLC at scale. Learners apply agentic coding for multi-file changes, integrate with CI/CD, review AI-generated code, and follow secure and reliable practices. The outcome is dependable AI-assisted engineering on real systems.
  • AI-Augmented Software Engineering — Ship better code faster with AI assistance

Related

Frequently asked questions

How long does this course take?

It runs roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code FUN-ENG.