Look, when you’re training a model to hold context, you’re actually training your own attentional bandwidth. There’s a real cognitive load to managing these exchanges. Daniel Kahneman mapped this out decades ago with System 1 and System 2 thinking—the difference between fast, automatic processing and slow, deliberate focus. Every time I catch myself dumping vague requests into Claude and hoping for magic, I’m outsourcing my System 2. And that’s where confirmation bias creeps in. I start seeing exactly what I want to see in the output. It echoes Jung’s old observation that we don’t see things as they are, we see them as we are. The model just amplifies whatever intention I bring to the prompt.

In my healing practice, I’ve noticed the exact same dynamic. Clients often want me to "fix" their field, but the real work happens when they learn to hold their own attentional space. It’s why the Hermit card keeps showing up in my spreads lately. Not the lonely, withdrawn version people often assume, but the figure holding a lantern and a staff, deliberately illuminating the path one step at a time. In computational terms, that lantern is your explicit context. When I stopped treating Claude Code as a black box and started treating it as a collaborative workspace, I had to slow down. I began structuring prompts with clear boundaries, defining variables before execution, and reviewing outputs with the same rigor I’d apply to a peer-reviewed manuscript. This shift didn’t just improve code quality; it forced a recalibration of my own cognitive habits.

Research on attentional control supports this parallel. Studies on mindfulness-based cognitive therapy demonstrate that training the brain to sustain focus on a single task reduces cognitive reactivity and improves metacognitive awareness (Kabat-Zinn, 2003; Teasdale et al., 2002). Optimizing an LLM session operates on a similar principle. You cannot multitask effectively when debugging a complex prompt chain. You have to be present with the syntax, the logic flow, and the iterative feedback loop. The model doesn’t guess your intent; it reflects your precision. Over time, this discipline bled into my daily practice. I found myself pausing before reacting, questioning my assumptions, and treating every interaction—whether with code, clients, or my own internal narrative—as a system requiring careful calibration.

Debugging, it turns out, is less about fixing errors and more about cultivating presence. When you strip away the noise and focus on the immediate context, you stop outsourcing your attention. You reclaim it. And in that space, between the prompt and the response, clarity emerges.