Custom Metrics Matter; Why One-Size-Fits-All AI Evaluation Doesn’t Work

When you’re building AI systems that touch the real world—whether they generate social media content, legal summaries, product recommendations or heck — even satirical startup applications—accuracy and latency alone aren’t enough to tell you whether your model is actually working.

Whatever you're wanting to evaluate, it all starts with a simple truth: accurate, fast, and useless AI applications are still useless applications.

The problem with standard evaluation

Most LLM evaluation pipelines rely on a narrow set of metrics. You’ll often see:

• Accuracy: Was it factually correct?

• Latency: How fast was the response?

• Token usage: How much compute did it consume?

• Safety: Did it generate anything harmful?

These are great for use cases like translation, summarization, or Q&A—where there’s a clear “right” answer. But they fall apart in more nuanced, high-impact domains, where:

• Success is subjective (e.g., humor, storytelling, marketing)

• Context is everything (e.g., medical advice, legal guidance, cultural content)

• Business value outweighs surface-level fluency (e.g., conversions, engagement, task completion)

And here’s the rub: the more specialized or context-heavy the use case, the less meaningful your default metrics become.  Sure they'll tell you if an API was called or if your application racked up an expensive OpenAI bill, but will they tell you if it was actually worth it? .

Why? Because impactful context requires human expertise. If your model is operating in a domain that professionals spend years training in, you’ll need domain-specific metrics to evaluate its success—and the input of real subject matter experts to define them.

Put simply: many real-world AI systems don’t have a single right answer. So your evaluation framework shouldn’t expect one.

Take the Startup Simulator 3000 app, for example. Factual accuracy was irrelevant. The goal wasn’t to describe real companies—it was to parody startup culture in a way that landed with human audiences.

Success meant making someone laugh, not getting the business model “correct.” It was on how to generate satirical content that made people laugh while accurately parodying startup culture.

Taking it from concept to reality

Understanding that you need custom metrics is a great starting point—but bringing them to life in a production environment requires a thoughtful, step-by-step approach. Whether you're building a medical assistant, a contract summarizer, or a comedy bot, the process of operationalizing evaluation often begins the same way: with simplicity, subject matter expertise, and iteration.

Here’s how to bridge the gap between a good idea and a real, scalable evaluation system:

1. Binary over continuous scoring

One of the first decisions you'll make is how to structure your metrics. While it's tempting to rate things on a scale from 1 to 10, this introduces more ambiguity than clarity. A “7” means something different to each evaluator—and those differences compound quickly.

Binary scoring—True or False, Pass or Fail—forces clear definitions of success. It enables:

• Consistent implementation across teams and model versions

• Actionable data developers can respond to quickly

• Evaluator alignment, minimizing confusion and subjective drift

• Simplified automation, making it easier to codify thresholds and trigger retraining or rollback processes

This doesn't mean you have to give up nuance—it just means that the nuance happens in the design of the metric itself, not the scoring scale.

2. Involve domain experts early and often

If you’re building for a complex domain, you can’t define “quality” in a vacuum. Subject matter experts (SMEs) are essential to shaping evaluation criteria that reflect the actual expectations, language, and workflows of the space.

If you’re building for a complex domain (or niche, specialized fields), you can’t define “quality” in a vacuum. Subject matter experts (SMEs) are essential to shaping evaluation criteria that reflect the actual expectations, language, and workflows of the space.

Bring experts in to:

• Define the success criteria from a practitioner’s perspective

• Identify high-risk failure modes (e.g. legal misinterpretation, clinical error, tone violations)

• Spot edge cases and ambiguity that a generic metric would miss

The best results when SMEs are involved not just in designing metrics—but also in helping review outputs during early phases of evaluation.

3. Start small, scale systematically

A common mistake we see teams make is trying to design an evaluation system that measures everything from day one. Don’t. Start narrow, iterate fast, and layer in complexity only when it’s useful.

Here’s a simple roadmap to get going:

1. Identify Success Criteria Ask: what does “good” look like for this model in this use case?

2. Define 3–5 Core Metrics Focus on the most important aspects of output quality. These should tie directly to business goals, user trust, or risk management.

3. Implement Binary Evaluators Write clear logic or rubrics to assess each output as a pass/fail. This is especially effective for QA processes and labeling at scale.

4. Involve Domain Experts Let them validate the metrics, calibrate scoring rubrics, and provide early feedback loops.

5. Version Your Evaluators Track changes to metrics over time. This ensures traceability—so when results improve (or regress), you know why. (PSSST — metric versioning is now available right within Galileo)

6. Automate Incrementally Begin with human-in-the-loop evaluation. As metrics mature and prove reliable, you can further automate them using model validators, LLM-as-a-judge techniques, or system tests.

How to build your custom metrics stack

Looking for the tl;dr on how to build the relevant metrics for your application? I've got you covered.

1. Start With the Goal – What does a successful output look like?

2. Bring SMEs Into the Loop – They know what quality means in your domain.

3. Blend Your Metrics – Use structural, behavioral, perceptual, and operational layers.

4. Track Over Time – track how metric performance changes over time through looking at your metrics in your evaluation tool of choice (biased, but I would choose Galileo here).

5. Set Flexible Thresholds – Acceptable ranges > rigid correctness.

6. Keep a human in the loop — custom metrics yes, will help, but in cases of high compliance, or risk, set aside time for human review.

The bottom line

Custom metrics aren't just nice-to-have if you are building quality domain-specific applications—they're your control system for delivering real value. Whether you're generating comedy, legal documents, or product descriptions, the principles are the same:

1. Measure what users care about—not what's easy to track

2. Make your evaluation decisions actionable

3. Involve domain experts early

4. Version and track your evaluation criteria

5. Automate incrementally

Generic metrics optimize for generic performance — and with the democratization of technology, generic no longer is the status quo,  If you want your AI to excel at specific tasks, you need specific measures of success.