#Llm

Command line interfaces used to be the domain of automation experts who knew how to wield the tool with precision. They scripted pipelines, chained commands, and bent systems to their will from a blinking cursor. That hasn’t gone away, but something new is happening. Large language models are now picking up these tools and wielding them just as effectively.

The key is design. If a CLI has clear help text and well described flags, an LLM can step in like an apprentice who suddenly knows the whole manual by heart. Add the ability to output JSON or another structured format, and the tool becomes not just usable but consumable. The LLM can run the command, parse the result, and carry the output forward into the next step.

If you are using VSCode or any other non-integrated editor (even vim or emacs), chances are you are already using a language server. These servers power features that are specific to the language or framework you are working with. They provide documentation, autocomplete, code navigation, warnings, and more.

When you click on a function and jump to its definition, a language server is likely behind the scenes making that possible.