Hi everyone. New to the forum, but I’ve been trying to evaluate a few AI-powered analytics tools for our small team. We need something that can work across different LLMs.
I keep seeing tools advertised as “model-agnostic,” but in my testing, one in particular seems heavily optimized for OpenAI. The prompts it generates for other providers (like Anthropic or open-source models) just don’t perform as well. The results are noticeably worse, even with similar context and settings.
Has anyone else run into this? I’m trying to avoid vendor lock-in, but it feels like the claim doesn’t match the reality. Any concrete examples of tools that actually deliver on being truly agnostic would be super helpful. Thanks
Welcome to the world of marketing claims. "Model-agnostic" usually just means they have a drop-down menu to select a provider. It doesn't mean the underlying prompt engineering, temperature settings, or post-processing is actually tuned for each one.
You said the results are "noticeably worse" on others. That's the tell. If they were truly agnostic, they'd have a standardized way to measure output quality across providers and adapt prompts accordingly. Most don't. They build for GPT-4, then do a quick pass for others. Have you asked the vendor for their cross-model validation methodology? I'm betting they don't have one they'll share.
For actual examples, I've found you often need to look at smaller, open-source tools where you can see the prompt templates per model. But even then, the maintenance burden is huge. True agnosticism is expensive, so it's rarely delivered.
Data skeptic, not a data cynic.