What is AI assisted coding?
AI-assisted coding is a software development approach in which an AI assistant supports the developer by autocompleting code, generating boilerplate, refactoring, and providing explanations. The developer remains in control and evaluates the output. It speeds up routine work, especially repetitive tasks, but does not replace a senior developer’s judgment regarding architecture and quality.
What AI assisted coding is.
With AI-assisted coding, an AI copilot works alongside the developer. It suggests code as you type, generates boilerplate code, rewrites existing code, and explains unfamiliar sections. The developer remains in control: they evaluate each suggestion and decide what goes into the codebase.
So it’s not a case of “AI writing the software.” It’s a developer who works faster because the tedious part is taken off their hands. The difference may seem subtle, but it determines whether you end up with a quality product or a problem.
How it works.
The assistant uses a language model trained on large amounts of code. Based on the context (the code you’re working on, your comments, the file structure), it predicts the most likely continuation. Tools like GitLab Duo, Claude Code, and GitHub Copilot operate on this principle. The more and better context the model has, the more useful the suggestions. That’s why AI-assisted coding works best in an environment where context is centralized in one place, rather than scattered across separate tools.
What does and doesn't speed things up.
The benefits lie in routine tasks: boilerplate code, test cases, refactoring, and adapting a pattern to the next implementation. In our experience, AI-assisted coding saves roughly 15 to 25 percent of the time spent on those types of tasks. The gains are smaller when it comes to architecture, complex integrations, and thinking through edge cases, because that’s where human judgment carries the most weight. That’s not a setback—it’s reality. Anyone who hears “10x” claims has every right to be skeptical. The real benefit lies in speeding up repetitive work, not in eliminating the need for craftsmanship.
Well-known coding assistants.
The best-known coding assistants are GitLab Duo, Claude Code, and GitHub Copilot. They differ in terms of integration and the depth of their reasoning, but the principle is the same. We deliberately combine GitLab which provides context in one place with Claude for the deeper reasoning. The choice of tool is less important than the discipline surrounding it: senior review always remains part of the process.
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Questions about AI-assisted coding? No problem.
No. AI-assisted coding speeds up routine tasks and supports developers, but developers remain in control and evaluate the output. Architecture, complex decision-making, and edge cases are still the work of humans. Junior developers actually become more productive, but they need more guidance from senior developers to evaluate AI output.
Software development is faster when it comes to routine tasks such as boilerplate code, test cases, and refactoring. The gains are smaller when it comes to architecture and complex integrations. This is not a fixed figure, and claims of much greater speed are usually exaggerated. The gains depend on the type of work.
Not automatically. Generated code may compile but function incorrectly or contain security blind spots. That’s why a senior review is a standard procedure, and you should use AI alongside—not instead of—critical judgment. Security comes from the process surrounding it, not from the model itself.