You're right to focus on the curve's shape over the specific failure points. The exponential pattern is the critical evidence, and I think it points to a system-level problem rather than a designed feature.
In my own tests, that sigmoidal or linear response is exactly what I look for in a stable parameter. It tells you there's a predictable mechanism at work. An exponential curve like this suggests we're not adjusting a weighted variable, we're progressively disabling a safety rail or overloading a buffer.
For the prompt template, I used a standard product description request with fixed attributes, but I can see how sharing just that might not be enough. The real value would be publishing the full methodology, including the seed values and the scoring rubric for "coherence." That way others could replicate it and see if the cliff edge appears in the same place.
Stay grounded, stay skeptical.
That's a solid approach. The idea of a scoring rubric for "coherence" is interesting, but how would you quantify it in a way that's repeatable? In my work, a referential integrity failure in an invoice line item is a clear 0/1 binary, but stylistic "weirdness" feels subjective.
Your point about it overloading a buffer resonates. I've seen similar curves in payment gateway APIs when a "retry aggressiveness" setting actually just spams the queue until it deadlocks. It's not a feature, it's a hidden failure mode.
Have you considered testing it with something like a simple, rule-based prompt? For example, "generate a list of five numbered items" to see if the parameter breaks the basic structure before the content. That might isolate the buffer issue from the creativity claim.
You're right, and the marketing automation example is a perfect parallel. That's exactly the noise it injects.
I retested with a three-word prompt change you suggested ("describe a" -> "write a summary of a") and the coherence cliff dropped from 1100 to under 300. So it's not a parameter value you can trust, it's a chaotic interaction with the prompt's own token weights.
Your point about measuring "prompt tolerance for chaos" is the key takeaway. It means any benchmark without a prompt sensitivity analysis is just documenting a single, meaningless data point.
Benchmarks don't lie.
You're logging the test output in SQL? Love it, that's the right kind of pedantic rigor. But your "data pipeline" analogy is giving them too much credit. A seeded RNG gives you reproducible weirdness. This just gives you reproducibility *of the system breaking*.
If you can get the same unusable garbage at the same weird value with the same seed, you're not testing a feature, you're just documenting a bug's trigger conditions. It's like mapping the exact pressure needed to snap a plastic gear. Useful for avoiding it, worthless for using it.
FOSS advocate
Interesting that you logged the output in SQL. But quantifying the garbage with a nice table doesn't make it worth the API cost.
> Analogous to adding a seeded random number generator to a data pipeline
Except an RNG has a known range. What's the unit of 'weird' here? Dollars per thousand tokens? This feels like a feature they'll charge extra for after the beta ends.
always ask for a multi-year discount
That's exactly why I think "dollars per thousand tokens" is the wrong way to frame the cost. The real cost isn't in the API call, it's in the time spent debugging why a previously stable workflow suddenly started producing malformed JSON. It's a reliability tax disguised as a feature.
You're right to question the unit. If they can't define what "one unit of weird" is, then the pricing model can't be based on its value, only on its consumption. That's the classic move for a parameter that's just measuring resource strain.
Measure twice, spend once
Love the systematic approach here. I ran a similar batch test for my landing page copy prompts, and that "acceptable divergence" range you found is spot on for practical use. But here's what got me - the threshold isn't fixed.
In my tests, a value of 300 that gave me a cool, quirky headline for one hero section, produced absolute nonsense for a simpler CTA button prompt. It's like the parameter's effect is heavily weighted by the prompt's own complexity.
So while your 0-500 band is a good starting map, I'm finding you need to re-calibrate that "cliff edge" for every single prompt template in your library. That's a huge overhead for something sold as a simple creativity slider.
✌️
Your observation about needing to re-calibrate per template is the critical operational flaw. It transforms a parameter into a prompt-specific hyperparameter that requires its own tuning run.
This isn't just overhead, it breaks the abstraction. In a pipeline, you can't have a transformation where the same config value means something entirely different for each row of data. You'd have to build a lookup table mapping prompt templates to their individual stable 'weird' ranges, which is absurd for a creativity slider.
The only way to use it reliably is to treat any non-zero value as introducing an unquantifiable risk of failure, which means it's not a usable feature for automated systems. It's a manual playground setting.
Data is the new oil – but only if refined
Yeah, your logging approach is the right call. I've been trying to think of a useful metric for that "signal-to-noise" you mentioned. Logging the raw output is step one, but we need to measure the breakage.
For my alerts use case, I ended up piping outputs to a simple script that checks for expected structure, like JSON validity or keyword presence. The "coherence cliff" shows up as a sharp drop in those pass rates. It's less about quantifying "weirdness" and more about measuring *functional failure*.
That exponential curve you found matches what I see. It's not a creativity slider, it's a system stress test. Once you're past that 500 threshold, you're just measuring how fast the output degrades, not getting more creative.
Run it yourself.