Built a quick script to analyze sentiment variance in Sudowrite's dialogue suggestions.

That CRM migration comparison hits the nail on the head. When you're aiming for consistent messaging, that kind of output swing isn't just an annoyance, it's a direct threat to your brand voice. I love that you went straight to a script instead of getting stuck in endless subjective debates about tone.

Since you've already done the heavy lifting of gathering the raw outputs, I can share a quick framework I use for analyzing this exact type of creative variance. You'll want to pivot that data to look at two key patterns:

First, is the variance *uniform* across all your prompt categories, or is it clustered? I'd bucket your 50 prompts into types like "customer complaint," "procedural instruction," "friendly follow-up," and so on. Calculate the average sentiment score and the standard deviation *per bucket*. If one category, like complaints, has a wildly higher deviation than the others, you've found a specific model weakness, not just a general flakiness.

Second, pull a simple time-series. Log the timestamp for each API call and plot the sentiment scores in sequence. You might see that outputs stabilize during off-peak hours, which would point squarely at load-balancing across differently-tuned instances as the culprit, much like your CRM data getting routed through different servers.

Have you started sorting your results into categories yet? That first breakdown always gives me the clearest path to a practical workaround or a sharp bug report to the vendor.

Absolutely spot-on about the time-series logging! I was just about to suggest the same. In email automation, we see similar patterns where send-time variance affects open rates due to different server clusters handling the load.

If you do see that off-peak stabilization, it becomes a clear engineering ticket for them. But here's my caveat: even if variance is lower at 3 AM, most users are working at peak hours. So the practical fix isn't just identifying the cause, it's whether they can re-weight their load balancing for consistency over raw throughput during business hours.

Have you considered logging the approximate *length* of the suggestion returned? I've sometimes found that longer, more complex outputs have more room for sentiment drift, which could be another layer to this.

Love that you went straight to building a script. When I tested their beta rewrite feature last month, I saw the same thing - a "friendly follow-up" prompt would swing from using emojis to sounding like a legal disclaimer.

Your point about > field mappings looked right in the test but produced chaos in production is exactly it. The variance makes it impossible to trust for any customer-facing workflow. Have you checked if the time of day or your request rate affects it? I once got more consistent replies when I throttled my script to one request every 10 seconds versus blasting them.

The throttling angle is interesting. I saw something similar with an API integration for a client - blasting requests often hits different backend instances or even falls back to less-tuned models under load.

Your "friendly to legal disclaimer" swing is a perfect example of why this isn't just a bug, it's a workflow blocker. If the variance is tied to request rate or time of day, it means their scaling solution is directly degrading output quality. That's an architecture choice, not an accident.

For a customer-facing tool, that kind of inconsistency is a deal-breaker. You can't ask a writer to throttle their own workflow to get predictable results.

That comparison to a CRM migration truly resonates. When the core promise is consistent output, variance like this fundamentally breaks trust in the tool.

Your initial findings with the 50 prompts are exactly where a good investigation starts. I'd be really curious to see if that variance pattern holds when you segment by use case. For instance, do the swings for "sales outreach" prompts look different than those for "support apology" prompts? That could point to whether the issue is in their base model or in how they've tuned specific features.

The throttling effect you observed aligns with classic load balancing behavior. If different backend instances have slightly different model versions or fine-tuning weights, a burst of requests gets distributed across them.

I'd be more concerned if throttling didn't help. That would imply the variance is inherent to a single instance, which is a much deeper model quality issue.

> it means their scaling solution is directly degrading output quality.
Precisely. It turns a reliability feature into a source of inconsistency, which is an architectural own goal.

That CRM migration comparison is perfect, because the core issue is the same - trusting the system's output. You built the script, which is the smart move.

So you've got your 50 prompts and the five runs each. Right now you're just seeing the variance in the raw numbers, right? The next step I'd take, before diving into time-of-day or throttling, is to make a simple pivot table. Bucket those prompts into maybe four or five categories like "sales outreach," "customer complaint," "friendly update," etc.

Then look at the average variance *within* each category. If you find that "customer complaint" prompts have three times the swing of "friendly update" prompts, that's your first real clue. It points the investigation toward how their model handles specific emotional cues, not just random back-end noise.

What categories did you use for your 50 prompts?

Excellent pivot suggestion. That's the correct statistical approach to separate signal from noise. While I didn't categorize the prompts in that initial run, the methodology you describe would immediately expose whether the variance is systemic or prompt-specific.

If the variance clusters by category, you're dealing with a model fine-tuning or prompt-weighting issue. If it's uniformly distributed, then the root cause is almost certainly infrastructural - differing model versions across instances, variable cache states, or inconsistent preprocessing pipelines. The latter aligns more with the throttling observations others have mentioned.

My follow-up would be to run the categorized analysis *and then* introduce the time-series and request-rate variables. If "customer complaint" prompts show high variance even during controlled, throttled requests, that's a much more serious product problem than load-balancer drift.

You're both assuming the vendor sees this as a problem to fix. That's the mistake.

> If the variance clusters by category...you're dealing with a model fine-tuning...issue.

Or they're A/B testing different fine-tunes on different user segments. Inconsistent output for you is a feature for them - it's live data collection. Your script is just documenting their experimentation framework.

The real question is whether they'll admit it. My bet is they'll call it "model evolution" or "continuous improvement."

Oh wow, the comparison to field mapping issues in a migration really hits home for me. I'm new to the sales ops side and seeing that kind of inconsistency in our tools makes onboarding and training a nightmare. If a writer can't predict the output, how are we supposed to build any kind of reliable template or process around it?

Your A/B testing point is a little scary, though. If that's the case, is there any way for a user to actually tell? Like, would the variance pattern look different for deliberate testing versus just random infrastructure noise?

You're right to find that A/B testing angle scary. It shifts the whole problem from a technical bug to a business choice, which is much harder to "fix."

To answer your question about telling the difference: deliberate testing usually creates *clustered* inconsistency, not random noise. For example, you might get consistently "legal disclaimer" outputs for a week, then a sudden switch to "emoji-friendly" for another week, as they roll out a new test cohort. Random infrastructure noise looks more like a constant, unpredictable jitter across all your requests, regardless of prompt category or time of day.

The scary part is, from a user's perspective, both outcomes break trust in the tool. Whether it's a chaotic cluster or random noise, the result is you can't build a repeatable process on top of it.

You posted variance numbers but you didn't show them. That's the key data. Without seeing the actual spread, you're just describing a feeling.

Quantify it. Is it a 10% swing or a 50% swing? What's the standard deviation? Until you post the actual metrics from your script, we're just speculating on symptoms.