How are LLMs used in coding?

LLMs are used in coding to write, explain, debug, and refactor software — acting as an always-available pair programmer that can turn plain-English requests into working code. It's become one of the most valuable and widely adopted uses of the technology, and for many developers it's now a normal part of the daily workflow.

What can an LLM actually do while you code?

The day-to-day uses are concrete:

• Generate code — produce a function or a whole file from a plain description.
• Explain code — walk through unfamiliar code and say what it does.
• Find and fix bugs — spot the likely cause and suggest a patch.
• Write tests — draft the checks that confirm code behaves.
• Translate and refactor — move between languages or clean up messy code.
• Handle boilerplate — knock out the repetitive setup nobody enjoys.

How do developers use these tools in practice?

Most developers don't paste code into a separate chatbot all day. Instead they work through AI assistants built right into the editor, which suggest code as you type and can answer questions about the file you're in. Increasingly there are also agentic tools that go further — they can read across a whole project, make coordinated changes to several files at once, and run commands to complete a task rather than just handing back a snippet.

The trend is toward the AI doing more of the mechanical work while the developer directs it.

What's the catch with AI-written code?

The same caution applies here as everywhere else with LLMs: they can produce code that looks right but is subtly wrong, insecure, or out of date. The model is predicting plausible code, not verifying that it runs correctly or safely. So the output needs review and testing — it's a powerful assistant, not a replacement for understanding what the code does.

Used well, LLMs don't just make coding faster; they shift the job toward describing intent clearly and reviewing results carefully, rather than typing every line by hand.