Why Reading the Chain of Thought Matters More Than the Code (Sometimes)
A reflection on how staying inside the reasoning loop — instead of passively waiting for results — makes AI coding more efficient for big changes and critical fixes.
One of the mistakes I made early on with AI coding tools was treating them like vending machines. I'd throw in a big request, wait, and only check the result at the end. It worked sometimes, but when the task was critical—like a tricky bug or a big service change—that passive approach slowed me down more than it helped.
When I was in "passive mode," I'd just wait for the model to finish. If it got stuck in a loop, missed context, or went down a rabbit hole, I only realized it after wasting time. The result might look polished, but the process underneath was often shaky.
In "active mode," I started reading along as the reasoning unfolded. Instead of just consuming the final output, I treated the chain of thought as the real work. If I noticed it drifting, I could interrupt, steer, or add missing references. That back-and-forth made the session feel more like pair programming than prompt engineering.
This shift also made me rethink how I structure my work. Jumping between multiple projects or terminals kills the focus you need to follow an AI's reasoning. Staying with one big change—whether it's a new service or a critical bugfix—lets me track the thought process closely and keep it on course. It's not how I handle every small change, but for the high-stakes ones, it's the difference between frustration and flow.
🛠️ Staying In the Loop in Practice
Following the chain of thought isn't just about watching words scroll by — it's about shaping the reasoning as it happens. Sometimes that means spinning up a quick AuthenticationArchitecture.md to keep the AI on track. Other times it's as simple as interrupting with:
- "We're using package A, not B."
- "Remember, this is a Next.js v15 app with a mix of client + server components."
These little nudges steer the model away from rabbit holes before they grow.
💡 Pro tip: If you catch yourself repeating the same correction more than once, bake it into your CLAUDE.md or Cursor rules. That way, the assistant carries that context automatically and you don't waste attention reminding it.
Be active in AI assisted coding
Not every change needs this level of focus. For small fixes or quick scaffolding, I still let the model run and check later. But for the big moves — services, architecture, critical bugs — staying inside the reasoning loop is what makes AI coding worth it.
It's less about treating the model like an answer machine, and more about treating it like a partner: one you keep an eye on, guide, and sometimes interrupt. That's where the efficiency (and sanity) comes from.