We hear it more and more: ‘Use LLM Evaluations to guide your AI project.’ And for a good reason—metrics are essential.
Yet, there’s a trap nobody talks about...
Let’s say you have a chatbot and want to introduce metrics. You find tools that compute metrics like 'Helpfulness', 'Conciseness', and 'Completeness'.
Sounds great—they promise to optimise your user’s experience. Right?
Truth is, their correlation to real business value is often unclear. Is this really what your user cares about ? Will this increase adoption ?
Many teams end up measuring the wrong thing, thinking they’re being data-driven, while forgetting about what really matters.
Metrics aren’t inherently good. They’re only as useful as the questions they help you answer.
If you don’t ask ‘What does success look like?’ or ‘What is the goal I want to measure?’ your metrics aren’t leading you—they’re misleading you.
So, the next time you set metrics, ask yourself: Are you measuring what impacts your business goals—or just what’s easy to quantify?
The difference might explain why your AI project feels stuck.
Because chasing the wrong metrics isn’t progress. It’s running in circles—like a headless chicken.
Top comments (3)
I agree Louis there is a lot of fake hype created in the market around LLM evals, but parallely there are tools that are doing really good. For example, I learned about this tool called Maxim AI through this blog on LLM hallucination detection. This blog led me exploring their tool, and my agentic workflows have become so streamlined that while working, I can really 'enjoy' my coffee now instead of drinking it just as a stress-buster lol ;)
Interesting! On my side I use Braintrust.dev and Langfuse for monitoring/eval, it looks like Maxim AI covers more or less the same space, I'll check it out to see exactly what they offer.
You said you are building agentic workflows, may I ask the scope of the project and if you are facing some limitations/blocking issues ?
I'm not specialised on agentic solutions, and I see many struggling to deliver value so I'm always curious to learn a little more about how people approach it :)
Love it!