Best Practices for Evaluations (Evals) for AI Solutions

AI evaluations ("evals") have emerged as the critical success factor distinguishing enterprises that successfully deploy AI solutions from those trapped in pilot purgatory. Only 10% of enterprises have Gen AI in production, and more than 30% of Gen AI projects are abandoned after POC. The number one reason why companies stall is a lack of trust stemming from Poor Performance (models hallucinate, exhibit unsafe behavior, or pose security risks), Unproven ROI (use cases are not adopted and targeted workflows remain unchanged), and Escalating Costs (unmonitored usage leads to extensive cloud or vendor bills).

When working on an AI use case, building a working system is only half the battle. How do you know if your AI project is actually doing what you want it to do? This is where “evals” (short for evaluations) come in. Evals help us measure how well our AI performs, spot problems before they become disasters, and give us confidence before we launch anything to users. In this post, we'll break down what evals are, why they matter, and some simple ways to get started.

For technology leaders, evals represent far more than technical validation—they are the foundation for building organizational trust, ensuring regulatory compliance, and demonstrating measurable business impact.

What are AI Evals?

At its core, an “eval” is just a way to test how well your AI system is doing its job. Imagine you built a spam filter - how would you know if it’s catching the spam emails but letting through the good ones? You’d need some way to check, right? That's evaluation in action.

In AI, evals usually involve running your AI project on some data you already know the answers to and seeing how well it consistently returns those answers. The goal is to have a clear, honest picture of your AI’s strengths and weaknesses before putting it out into the real world.

Evals should be front and centre of any AI project and arguably the first thing you do before you even write a line of code.

Why are Evals Crucial in AI?

Evaluations are more than just a checkbox - they’re your early warning system. Traditional software testing focuses on deterministic outcomes—given input X, the system should produce output Y. AI evaluation operates in a fundamentally different paradigm with:

Non-Deterministic Behavior: AI systems can produce different outputs for identical inputs, requiring statistical rather than binary assessment methods. If you ask the same question several times you may get different answers, some of which may be plausible but incorrect. This means they can sometimes make mistakes that are hard to spot or explain.

Contextual Performance: AI effectiveness varies dramatically based on domain, data quality, and use case specifics, demanding customized evaluation approaches.

Emergent Capabilities: AI systems often exhibit unexpected behaviors and capabilities that weren't explicitly programmed, requiring comprehensive safety and robustness testing.

Continuous Evolution: Unlike traditional software that remains static between releases, AI systems can drift over time, requiring ongoing evaluation and monitoring.

Common Eval Workflows

So how do people usually perform evals in AI projects? Here are some simple steps that show up in most workflows:

• Build a dataset that represents the task you’re trying to do: If you’re building a spam classifying AI agent, you’d want a dataset of emails that were already categorised as spam or not spam.

• Test and Compare: After you have a version ready, you test it using the dataset you built. You check its answers against the real answers (“ground truth”) to see how many it got right (or wrong).

• Repeat Regularly: Data and user behavior can change over time. Good teams run these evals regularly to make sure their AI performance doesn’t drift or degrade.

• Look for Patterns: Beyond just one number, look for areas where your AI project does especially well or badly - this can help target future improvements.

Best Practices and Pitfalls

1. Start with Business Context, Not Technical Metrics

Define Success Criteria Before Development

The most common failure in AI projects is retrofitting evaluation to completed solutions. Instead, technology leaders should establish clear success criteria during the problem definition phase:

• Business Impact Metrics: What specific business outcomes will the AI solution improve?

• Performance Thresholds: What minimum accuracy or efficiency levels are required for business value?

• Risk Tolerance: What error rates are acceptable given the business context?

• User Experience Standards: How will end-users measure solution effectiveness?

Example Framework for Success Definition:

Business Objective: Reduce customer service response time by 40%
Success Criteria:
- AI solution handles 70%+ of routine inquiries without human intervention
- Customer satisfaction scores maintain >85% for AI-assisted interactions
- False positive rate for escalation recommendations <5%
- System availability >99.5% during business hours

2. Implement Multi-Dimensional Evaluation Strategies

Industry-Standard Benchmarks

Leverage established benchmarks to assess AI capabilities against known standards:

For General Intelligence: MMLU (Massive Multitask Language Understanding) evaluates models on multiple choice questions across 57 subjects, including STEM, humanities, social sciences, and more, with difficulty levels ranging from elementary to advanced.

For Domain-Specific Applications: BIRD benchmark measures text-to-SQL and draws from 95 large databases covering topics as varied as blockchain, hockey, and healthcare. An AI system needs to transform a question like, "What percentage of soccer players born in Denmark weigh more than 154 lbs?" into executable SQL queries.

For Financial Applications: S&P AI Benchmarks by Kensho assesses and ranks the quantitative reasoning abilities and expertise of large language models across the business and finance industry.

Custom Evaluation Datasets: While industry benchmarks provide valuable baselines, enterprise AI solutions require custom evaluation datasets that reflect:

• Domain-Specific Terminology: Industry jargon, regulatory language, and company-specific vocabulary

• Real-World Data Distributions: Actual data patterns, edge cases, and quality issues from production environments

• Business Process Context: Workflow integration points, decision-making scenarios, and stakeholder requirements

3. Establish Continuous Evaluation Infrastructure and Processes

Automated Evaluation Pipelines

For a consistent, repeatable evals process, aim to implement tools and processes that combine automated evaluations with an expert workforce for human evaluations to build a "Trust Feedback Loop" of evaluation, improvement, and monitoring. 

Ideally, look to implement automated infrastructure that can:

• Monitor Production Performance: Track accuracy, latency, and user satisfaction in real-time

• Detect Performance Drift: Identify when AI systems begin to degrade over time

• Trigger Alerts: Notify teams when performance drops below defined thresholds

• Generate Improvement Recommendations: Suggest specific interventions based on evaluation results
  
  
  

Human-in-the-Loop Evaluation

For complex, subjective, or high-stakes decisions, incorporate human evaluation:

• Expert Review Panels: Domain experts assess AI outputs for accuracy and appropriateness

• User Feedback Integration: Capture and analyze end-user satisfaction and effectiveness ratings

• Adversarial Testing: Red team exercises to identify potential failure modes and vulnerabilities

To get the most out of evals, keep these best practices in mind:

• Don’t Rely on Just One Number: It’s tempting to look at a single score and call it a day, but dig deeper. Sometimes an AI project that looks “good overall” still fails for certain users or types of data.

  
  

• Break down longer workflows into shorter steps: A common failure case for AI projects is simply trying to do much with a single prompt. You should look to break projects down into smaller, separate steps and evaluate each step separately. This ensures that the model gets it right at every point.

• Don’t Ignore Surprising Results: If your evals turn up something weird, investigate. Sometimes this reveals hidden bugs, data issues or new failure cases.

• Evals aren’t everything: Vibes are important too! Even if something produces great eval numbers it can still feel “off”. This is AI after all. You still want to get hands on with testing and getting customer feedback.
  
  

• Look for common industry datasets: Using common benchmarks (eg BIRD for text-to-SQL) can give you a handle on how well you’re solving the problem compared to others. Plus, having ready-made datasets can save you time and resources.
  
  

• Stay Transparent: Record your evaluation results and share them with your team. If something goes wrong later, you’ll have a record of what you tested and why.

Conclusion & Next Steps

Evaluations are the secret weapon for any AI project. They help you plan your project, build trust, and improve over time. Even simple evaluation steps can make a big difference, especially as you’re getting started. As you gain more experience, you can explore more advanced metrics and tools - but the basics will always matter. If you don’t measure it, you can’t improve it!

Ready to dive deeper? Try running some simple evals on your next AI or data project, and please get in touch if you’d like to build an AI proof-of-concept with Minds Enterprise. The sooner you start, the better your results will be.