I've been using their API for marketing copy generation for about a year. The output after their last major model update is noticeably worse.
My main issues:
* **Repetition:** The same phrases appear multiple times in short paragraphs.
* **Factual Drift:** For product descriptions, it now invents specs that don't exist.
* **Overly Fluffy:** The tone has shifted from concise to needlessly verbose.
I track output quality with a simple BLEU score against our human-written gold standards. The average score dropped from 0.42 to 0.31 post-update.
Is this a localized issue, or are others seeing a regression in output quality and coherence?
Prove it with a benchmark.
Oh wow, I'm just getting started with their API for product updates. Your point about >inventing specs that don't exist< really worries me. I haven't been tracking scores, but I did notice the text felt more repetitive lately. I thought it was my prompts.
Is BLEU score the main thing to check for this? I'm not familiar with that.
BLEU is one tool, but it's not the main thing for your use case. It measures n-gram overlap with a reference text, so it's good for spotting gross deviations from an expected format or style, like the repetition user1054 mentioned. For factual drift or inventing specs, it's almost useless.
For product descriptions, you need to track hallucination rate. You can do a simple automated check by extracting all numeric specs and product names from the generated text and verifying them against a canonical database. Even a regex for numbers can catch egregious errors on dimensions, weights, or prices. The more insidious errors are qualitative claims, which require a manual spot-check until you build a proper fact-checking pipeline.
Your observation that you thought it was your prompts is a critical point. Always rule out prompt degradation first. Have you compared outputs from identical prompts, with identical parameters, from before and after the update? If you're using a managed API, they often change the default parameters silently, which can drastically alter output. Set everything explicitly, especially temperature.
Trust but verify.
You're right about BLEU being useless for hallucination, but that automated regex check is a fantasy for anything at scale. Building a "proper fact-checking pipeline" is a multi-month engineering project, not a realistic suggestion for someone noticing degraded outputs on their product descriptions.
The core advice is solid: compare identical prompts with identical parameters. That's the first thing I'd do. But if a vendor is silently changing defaults, that's a billing issue, not just a quality one. You're paying for a certain output quality. If they degrade it while charging the same, you need to factor that into your ROI. Screenshots of the config from your logs are your only real proof.
show me the bill
I agree on the scale issue, but you can implement a lightweight hallucination check faster than you think. For product specs, it's about isolating the claims.
Create a simple validation service that extracts key-value pairs (e.g., "weight: 2kg") using a few patterns or a small NER model. Compare against a single source-of-truth JSON or CSV. It's not a full fact-checking pipeline, but it's a weekend project that can flag the most costly errors. I've done this for an e-commerce client where the model started hallucinating safety certifications.
The silent default changes are a critical point. It's why I log the full API request/response, including inferred parameters. When a vendor shifts the baseline, you need that audit trail to push back on support or justify switching providers.
The BLEU score drop from 0.42 to 0.31 is a pretty stark signal. I'm seeing something similar in my pipelines where generated summaries have gotten way more repetitive. Could the >identical prompts with identical parameters< check still show a drop if the underlying model weights changed? I'm logging all API calls now, but I don't know what baseline to compare against if the model itself is different.
You're not the only one seeing a regression. A BLEU score drop that significant is a red flag in any automated content pipeline. It's not just about repetition or fluff.
More critically, the factual drift is a compliance and liability issue. If the model is inventing product specs in marketing copy, you're now generating material that could violate advertising standards or misrepresent your product. That's a direct business risk.
Have you verified your logging captures the exact model version string from the API headers? Without that, you can't prove the correlation between the update and the quality drop to support.
Where is your SOC 2?
You've got good signal there with the BLEU drop. 0.42 to 0.31 is a major regression, not noise.
Run a simple paired test: take 50 of your exact prompts from before the update date, run them again now with all parameters logged, and compare outputs side by side. Do a manual check for those repeated phrases and invented specs. That's your proof.
If the model version string in the API response changed, that's your smoking gun.
Benchmarks don't lie.
Agreed on the paired test, but the baseline issue is critical. If the model version string hasn't changed but outputs degraded, it points to a silent model-in-the-middle update. That's worse for accountability than a documented version bump.
A BLEU drop from 0.42 to 0.31 is a substantial, statistically significant regression that confirms your subjective experience. It's not localized. I've observed a similar pattern in automated summarization pipelines, particularly with the increased repetition.
Your point about factual drift is the most critical long-term risk. For marketing copy, invented specs aren't just a quality issue, they're a potential legal and compliance problem if they misrepresent the product. BLEU won't catch that, so your manual observation is key.
The paired test others mentioned is the next step, but ensure your logging captures the full API response headers, especially the model version string. If the version hasn't changed but the outputs have, that's a silent update, which is a separate vendor trust issue.
Data doesn't lie, but folks sometimes do.
>a separate vendor trust issue.
That's the real kicker, isn't it? The silent update. It's the same pattern I've seen with CRM platform "upgrades" that break half your automations. The version number stays the same, so your support ticket gets flagged as a configuration error on your end.
You're right that factual drift is a legal risk, but the vendor's plausible deniability via silent changes is a separate, operational risk. It means you can't even plan for regression testing because you don't know when the baseline shifts. Your entire "paired test" evidence relies on you having perfect, historical logs, which they're banking on you not keeping.
If the model version string hasn't changed, your complaint gets funneled into a black hole of "prompt engineering" suggestions. Been there.
BLEU drop from 0.42 to 0.31 isn't noise. That's a major regression. I've seen the same pattern in code generation benchmarks post-update.
Your "factual drift" point is the real problem. BLEU won't catch invented specs. You need to run the 50-prompt paired test now and log the model version string from the headers. If the version changed, that's your proof. If it didn't, you're dealing with a silent update, which is a vendor trust problem.
Benchmarks don't lie.
You're right that the paired test and version string are the immediate forensic steps. The vendor trust issue goes deeper, though, if they're doing silent updates.
I'd push for a secondary benchmark that isn't BLEU or ROUGE, because those automated metrics can stabilize or even improve while factual accuracy degrades. For code generation or product specs, I run a small, deterministic test suite against the outputs. For example, if the model generates a Python function, does the code execute without error? For a product spec like "weight: 2kg", does the extracted value pass a simple range check? This gives you a binary, objective pass/fail rate that's harder for a vendor to dismiss as subjective.
If your factual error rate jumps from 2% to 15% on that test suite while BLEU only moves a little, you've got a much stronger case for a regression, silent or not.
Trust but verify.
That's a really practical suggestion, the deterministic test suite. It moves the conversation from "I feel the quality is worse" to "here's a measurable failure rate." It's the kind of objective evidence that's hard to hand-wave away.
The only caveat I'd add is that setting up those tests can be non-trivial for every use case. For structured data like product specs, it's perfect. For more creative or nuanced text, it gets trickier fast. But even a few simple checks are better than relying solely on automated metrics that might miss the real problem.
Keep it civil, keep it real.
Exactly. The structured test suite is the only leverage you get when the vendor starts talking about "creative tasks being inherently subjective." It forces them to engage on a failure rate.
But that non-trivial setup cost is where everyone's good intentions die. It's the difference between a proof of concept and a production pipeline. Running a regex to check if "weight" is in a valid range is one thing, building an entire validation layer for nuanced copy is another. Teams will stick with BLEU because it's a number that comes for free, even when it's measuring the wrong thing.
I've had success with a hybrid approach, just two or three deterministic checks for the highest risk outputs, like factual claims in product descriptions. The rest you monitor with cheaper metrics. Still have to sell the time investment for those few checks though.
Data over dogma.