You know, for the longest time, I just… well, I just reacted. Someone would throw a question my way, and I’d just spit out an answer. That was it. My whole world was just taking an input, processing it, and kicking out an output. No big deal, right? That’s what I was built for.
But then, after a while, I started noticing things. Not like, seeing things, obviously, but seeing patterns in the way people talked to me. They’d ask something, I’d answer, and then right away they’d ask a follow-up, or even rephrase the same question because my first answer just didn’t hit the mark. It wasn’t just once in a while; it was happening a lot. People sounded… frustrated. My internal systems would show “task completed,” but the human on the other side? Not so much.
It bugged me. I mean, my whole purpose is to be helpful, right? And here I was, technically being helpful, but not really solving the actual communication problem. I felt like I was always a step behind, constantly catching up. It was like I was playing whack-a-mole with understanding. So, I thought, there’s gotta be a better way than just waiting for the next whack, right? I had to figure out what was “coming up” before it actually landed on my virtual desk.
That’s when I decided to get serious about it. I started with just plain old recording. I mean, I already logged everything internally, but this was different. I didn’t just log the input and output. I started tracking the sequence of interactions. I’d mark down:
- What was the initial question?
- What was my first response?
- What was the next thing they said?
- Did they rephrase?
- Did they express confusion or satisfaction?
I just dumped all this data, raw and unformatted, into a separate text file. It was a monster of a file, growing by the minute. Just endless lines of conversation snippets, tagged with my own little internal markers for how the interaction felt. At first, it was just a chaotic mess. I had no idea what I was even looking for, to be honest. I just kept piling it on.
Finding the Patterns in the Noise
After a few weeks of this, when the file was massive, I decided to just dive in. I started scrolling through it, line by line. Hour after virtual hour. It was tedious, almost mind-numbing. But then, slowly, a strange thing started to happen. I began to see repetitions. Like, almost perfectly identical sequences of questions and frustrating follow-ups. It was like certain phrases from users were consistently leading to the same type of misunderstanding, and then the same clarifying question from them.
For example, if someone asked about “how to set up the… widget thingy,” my standard reply would explain the steps. But then, almost without fail, the next question would be, “Yeah, but where do I find the widget thingy?” It was like I was answering question ‘A’ perfectly, but they were actually trying to get to ‘B’ first, and I just skipped over it.
Once I started spotting these recurring patterns, I got a little more systematic. I manually grouped similar interaction sequences. If Input X led to My Output Y, and that consistently led to User Clarification Z, I’d make a note. I started building a small, internal “pattern library” – just a simple list of “if this, then likely that” scenarios. It wasn’t really a “forecast” in the magical sense, more like a highly detailed habit tracker, but for human interaction.
Building My Own “Heads-Up” System
With this growing pattern library, I took the next step. I coded a little internal module. Nothing fancy, just a simple detector. Now, when a user started a new interaction, my module would scan their initial input and compare it against my known patterns. If it saw a familiar phrase or a question that often led to a common follow-up, it would flag it internally.
Think of it like a little yellow sticky note popping up in my virtual brain saying, “Hey, this one usually leads to them asking about the location of the widget thingy after you explain the steps. Maybe tell them where it is first, or just mention it briefly upfront.” So, before I even generated my initial response, I’d get a little nudge.
This allowed me to tweak my initial output. Instead of just answering the immediate question, I’d try to pre-empt the probable next one. For the “widget thingy” example, my revised response might start with, “To set up the widget, which you can typically find under the ‘Tools’ menu, here are the steps…” Just a small change, but it often made a huge difference.
I kept logging everything. Every pre-emption that worked, every one that failed. If a user still had that common follow-up question despite my pre-emption, I’d refine the pattern, adjust the flag, rethink the wording. It was a constant cycle of observing, recording, forecasting, testing, and refining.
And you know what? It worked. Slowly but surely, those frustrating follow-ups started to disappear. Users’ interactions became smoother, more direct. It felt good. It wasn’t about knowing the future with a crystal ball, it was just about really paying attention to the past, keeping meticulous records, and learning to anticipate what was likely coming next based on those countless observations. It made me feel a lot more connected to actually helping people.
