AI Systems Age Faster Than We Expect
by•
Here’s something that surprised us.
AI systems don’t just run, they age.
Prompts become stale.
Assumptions stop matching reality.
Small shortcuts compound quietly.
Nothing breaks overnight.
But reliability slowly erodes.
Maintaining AI isn’t just monitoring uptime.
It’s actively renewing assumptions.
Curious to hear from this crowd:
What part of your AI system needs the most “refreshing” today?
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Lightfern for Email
I’ve found that with every new model version, I need to re-review outputs to make sure quality hasn’t shifted. We use a synthetic data pipeline, but even small changes in how a model writes can subtly change the output distribution.
A lot of this feels like prompt overfitting -- similar to classic ML overfitting, but where prompts are implicitly tuned to a specific model version and don’t generalise cleanly to the next one. When we upgraded from gpt-4o to gpt-5, we actually saw a drop in performance.
Having proper evals helps :)
GraphBit
@dougli “Prompt overfitting” is a great way to describe it. Prompts silently bind themselves to model behavior, so upgrades can regress quality even when capability improves. This is why evals need to track distribution shifts, not just accuracy.
Totally agree AI drift is subtle but real. For us, keeping prompts and datasets up to date takes the most effort; without it, outputs slowly lose accuracy over time.
GraphBit
@lukas__lang Exactly. Drift shows up first as slightly worse answers, not failures. Prompts and datasets encode assumptions, and those assumptions age faster than most teams expect.
For us, the part that needs the most refreshing is the assumptions layer. User behavior, market signals, and even internal workflows shift fast so the prompt and context templates drift quietly. A small mismatch today becomes a reliability issue tomorrow. Constant recalibration is the only way to keep the system sharp.
GraphBit
@gbenga__ Well said. Assumptions are the quietest failure mode. When behavior or workflows shift, prompts keep enforcing yesterday’s logic unless recalibrated and that gap compounds fast.