Robert TaFounder & CEO, Clarity · Building Hyper-Personalized Agents

Your users don't care about coherence scores

19 Oct 2026 · 1 min

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In this brief dialogue, Robert Ta argues that value-alignment metrics (VALs) must be tailored to the specific application rather than relying on generic helpfulness or coherence scores that many vendors promote as industry standards. He warns that such off‑the‑shelf metrics give a false sense of confidence and can obscure real performance problems. Instead, he advocates for concrete error analysis that directly reflects whether the application works for its users. Speaker B echoes this stance, urging teams to examine their own data and design problem‑specific metrics instead of adopting generic frameworks.

Our VALs need to be application-specific. We can't just use some generic helpfulness score, but all the vendors sell them. It's an industry standard. It's fast. Generic metrics create a false sense of confidence. We need to do real error analysis. The guy throws over the table and chair. You're overcomplicating it. Our users don't care about a coherent score. They care that the application actually works.
Yeah. I mean, that meme is like, hey, like don't use off-the-shelf metrics. Don't use off-the-shelf frameworks. Like, look at your data, make metrics that are specific to your problem.
And I like this sort of backwardsness, which is the person that's saying, let's go with the generic thing is saying you're overcomplicating it. But it's like, but you actually are. You're, you're trying to bring in all these fake scores.
No, this, this meme is the kind of argument you are going to have as an AI builder, that as a practitioner, someone on your team is going to be like, let's just use the framework. And you're going to have to fight them just like this.