The AI Coding Partner’s Hidden Quirks: What You Need to Know Before You Rely on It

Moving from casual experimentation to trusted collaboration means understanding how these tools actually work—and where they quietly lose the plot.

You’ve tried the AI coding assistants. Maybe you’ve used them to write a function, explain some code, or fix a bug. Tools like GitHub Copilot and Cursor are quickly becoming part of the developer’s everyday toolkit.

In my previous article, I wrote about the big picture of how AI is changing software development. Next, I’ll share specific tips for tools like Cursor. But between those two articles lies something important: recognizing that these AI partners aren’t perfect logic machines. They have quirks. And if you don’t understand them, you might find yourself with messy, inconsistent code that looks right at first glance.

To work effectively with AI, you need to understand three core challenges: memory limits, attention bias, and creative control.

1. The Forgetful Partner: Memory That Fades

Every AI model has a “context window”—essentially, how much it can remember from your current conversation. This includes your instructions, its own replies, and any code you’ve shared.

The problem isn’t just hitting the limit. It’s that the AI’s memory fades as you talk more. Imagine you start a chat by giving clear instructions: “Use our company’s design patterns. Write clean, documented code.” You then spend the next hour discussing a specific feature. By the end, those initial instructions have faded into the background. The AI might still produce working code, but it could ignore your patterns or skip documentation—not because it’s disobeying, but because it genuinely forgot.

The lesson: Your most important rules need to be repeated or placed strategically. Don’t assume that because you said it once, it’ll be followed forever.

2. The Distracted Mind: What the AI Really Pays Attention To

Not everything you say carries equal weight. Two subtle but important behaviors explain a lot of frustrating moments:

• Recent Over Remote: AI models tend to focus more on what you just said than what you said a while ago. This “recency bias” means the last few messages can outweigh a whole page of earlier instructions. You can provide perfect examples at the start, but if your latest request is vague, the AI might follow the vagueness and ignore your perfect examples.
• Strong Start, Fuzzy Middle: Instructions given at the very beginning of a chat (sometimes called “system prompts”) often have a strong influence. But as the conversation grows, that influence weakens. Your carefully crafted coding standards can get buried under new code, questions, and outputs.

This creates a tricky situation: The more help you give the AI (by providing examples and context), the easier it is for your core rules to get lost in the clutter.

3. Controlling the “Creativity” (When You Can)

You might have heard of “temperature”—a setting that controls how random or predictable the AI’s output is. A high temperature makes it more creative and varied; a low temperature makes it more repetitive and predictable.

Here’s the practical catch: Most AI coding tools don’t let you adjust this directly. They fix it at a balanced, usually lower, setting to keep code sensible. This is generally good—you don’t want wild creativity when writing a database connection. But it means you’re working within the tool’s chosen balance. You can’t manually crank up “creativity” for brainstorming or turn it all the way down for strict boilerplate. You have to work around this by being very clear in your prompts instead.

Working With, Not Against, the Quirks

The goal isn’t to fight these behaviors, but to work with them:

• Reset the Memory: In a long chat, periodically restate your key requirements. Or, start a fresh chat when switching to a new task to give the AI a clean slate with your rules front and center.
• Structure Your Prompts: Put your most critical instructions early and, if possible, in a dedicated “system” or “rules” section. Gently remind the AI of them later: “Remember to follow the error-handling pattern we discussed.”
• Accept the Fixed “Creativity” Setting: Since you usually can’t change the temperature, focus on being explicit. For brainstorming, ask for “three alternative approaches.” For precise work, specify “use the standard library method” or “follow the exact pattern from the previous file.”

When you understand these hidden behaviors, you stop being surprised by the AI’s odd choices. You start anticipating them. You become less of a passive user and more of a skilled guide, steering a powerful but sometimes distractible partner toward the right outcome.

This mindset is the foundation for the next step: the specific workflows and prompts that make tools like Cursor truly transformative. That’s exactly what we’ll cover next time.