Building an AI Evaluation Strategy: How to Map and Measure What Matters

As teams rush to integrate large language models (LLMs) into products, a big question keeps coming up: How do you know your AI is actually working—safely, reliably, and in line with your business goals?

The answer isn’t more tools or off-the-shelf metrics. What matters most is having a clear evaluation strategy—one that connects your business risks to the right tests, and keeps you aligned as your AI grows more complex.

Why You Need a Strategy—Not Just Tools

Too many teams start by picking evaluators for toxicity, bias, or performance, hoping they’ll cover all their bases. But this “tools first” approach leads to confusion:

• Metrics that don’t match your actual business risks
• Dashboards full of data nobody acts on
• Compliance gaps when regulations or clients ask for specifics

Before anything else, define what matters for your business: Are you most concerned with safety (like fairness, toxicity, compliance)? Or is performance (relevance, accuracy, latency) your top priority?

From Strategy to Action: The Role of Taxonomy and Ontology

Once you have your strategy, the next challenge is mapping it to real-world evaluation. That’s where a well-structured taxonomy or ontology comes in. Think of it as your mental model for organizing all possible ways to measure your AI:

• Top-level: Safety vs Performance
• Under Safety: Fairness, toxicity, bias, compliance checks, robustness
• Under Performance: Accuracy, relevance, speed, reliability
• Each branch splits into specific evaluators or tests (e.g., bias → demographic parity, or relevance → retrieval accuracy)

This taxonomy-first approach lets you:

• See coverage and gaps at a glance
• Plug in new open-source evaluators as needed
• Adapt quickly as client, business, or regulatory needs change

How to Get Started: A Modular, Future-Proof Framework

1. Define your risks and goals first. For regulated sectors, start with compliance (see our AI Roadmap for this process).
2. Build your taxonomy or ontology. Organize every possible evaluator by purpose, modality, and enforcement intent.
3. Plug in the right tools. Choose open-source or commercial evaluators for each category.
4. Review and update. Make your evaluation map a living document as requirements change.

Move Beyond Tools—Build Your AI Evaluation Strategy Today

Don’t settle for random dashboards or disconnected tools. Build an ontology-first roadmap for evaluating your LLMs and AI products—so you can prove safety, performance, and compliance as you grow.

Book an AI Roadmap Session to map your evaluation strategy before you choose your next tool.