At Genfutures Lab, we help organisations stay ahead with the smart use of generative AI. But here's the truth: having the latest AI tools isn't enough. The real game-changer is knowing how well they perform and that’s where AI evaluations (or "Evals") come in.

Why are AI Evaluations Essential Right Now? 🧠

AI evaluations (or "Evals") are structured ways of testing how well an AI model performs for specific tasks. Instead of assuming the output is good enough, evaluations help you measure it: for tone, creativity, accuracy, and consistency.

This has never been more important. As new AI models and tools launch at breakneck speed, businesses face a new challenge: Can you actually trust what the AI produces?

Research from Stanford’s Centre for Research on Foundation Models (CRFM) shows that even the most advanced models can behave unpredictably, depending on the prompt, task, or domain. Without regular evaluation, businesses risk publishing content that’s off-brand, inaccurate, or lower quality than they realise, hurting credibility and missing opportunities for growth. ⚡

In short: evaluating your AI isn’t a “nice to have” anymore, but it’s critical to success.

What Should You Be Testing For? 📊

Good AI evaluations aren’t just about whether the model “works”, they measure how well it matches your goals.

Ask yourself:

• Is the tone right for your brand?
• Is the information accurate and up-to-date?
• Is the creative output original and engaging?

In one recent case study, a retail brand used two different AI tools to generate product descriptions. On the surface, both performed well. But after testing for tone consistency and factual accuracy, they found that one model was 35% more reliable, saving them hundreds of hours in editing time.

The lesson? Without structured evaluation, you might not even spot the gaps.

How to Start Evaluating AI Effectively 🔧

You don’t need to build a lab to start testing. Here’s a simple framework we recommend to our clients:‍

1. ‍Set clear benchmarks: Define what good output looks like for your brand (tone, style, quality).‍
2. Use a scoring system: Rate AI outputs based on consistency, creativity, accuracy and speed.‍
3. Test regularly: Models change fast with updates, what works today might drift tomorrow.‍
4. Involve human reviewers: AI evaluation should never be fully automated. Human judgement is key to maintaining quality.

Looking for a real-world example? Companies like Canva and HubSpot now run monthly AI quality reviews to make sure their content remains fresh, useful, and on-brand even as their AI tools evolve.

Final Thought 💬

At Genfutures Lab, we believe AI should be a creative ally, not a mystery box. Regular evaluations are the key to using it smartly and staying competitive.

Are you confident your AI tools are delivering their best? If you’re not sure where to start, join our upcoming workshop on “AI Audits: How to Test and Tune Your Generative Models” or get in touch for a consultation.

‍