What every developer should know about LLMs

March 23, 2026

What every developer should know about LLMs (before blaming the tool)

When someone says “AI wrote bad code,” we always ask: do you understand what you’re actually talking to?

Most developers interact with AI through agents — Copilot, Cursor, Claude Code. But the agent is just the driver. The engine underneath is an LLM. And if you don’t understand the engine, you’ll keep getting frustrated without knowing why.

An agent is not an LLM.

An LLM is a stateless function. It takes text in, predicts the next token, and repeats. No memory. No intent. No “understanding” of your project. Every call starts from a blank slate — the only “memory” is what fits in the context window.

The agent is the layer on top: it manages context, calls tools, reads files, retries on failure. When Claude Code fixes a bug across 5 files, the LLM doesn’t “see” all 5 at once — the agent feeds them in strategically.

Three things that actually change how you work:

  1. Break big tasks into small ones. The model has limited attention — the more noise in the request, the worse it focuses. “Refactor the authentication module” overwhelms it with decisions. “Extract token validation into a separate function” gives it a clear target. Smaller tasks = less noise = better results.
  2. Show what you want, not what you don’t. LLMs are built to continue patterns. Saying “don’t use any classes” forces the model to think about classes first. Instead, describe what you want and give a short example of the style you’re after. “Use functional style with filter/map chains” works dramatically better than a list of “don’ts.” Good examples beat long explanations every time.
  3. Iterate, don’t start over. Many developers treat each prompt as a one-shot exam. In practice, building incrementally — “now add error handling,” “make this async”, “rename for clarity” — consistently beats trying to get it perfect in a single request. Each refinement keeps the model focused on one thing at a time. Conversation is the interface, not a single command.

Our takeaway after months of daily use: the teams that learned these fundamentals got noticeably better results from the exact same tools. Not because of fancier prompts — because they stopped fighting the architecture and started working with it.

What’s the one LLM concept that changed how you use AI tools?