What is AI Evaluation (Evals)?
AI Evaluation, commonly called Evals, is the systematic process of testing and measuring AI system performance across quality, accuracy, safety, and reliability dimensions before and after deployment to ensure the system meets business requirements and user expectations.
What Is AI Evaluation (Evals)?
AI Evaluation — commonly referred to as Evals in the industry — is the practice of systematically testing AI systems to measure their performance, accuracy, safety, and reliability. Just as you would not launch a new product without quality assurance testing, you should not deploy AI systems without rigorous evaluation.
Evals go beyond simple accuracy metrics. They encompass a comprehensive assessment of how an AI system behaves across a wide range of scenarios, including edge cases, adversarial inputs, and culturally sensitive contexts. The goal is to understand not just how well the AI performs on average, but where it fails, how badly it fails, and what the business consequences of those failures would be.
The term "evals" has become standard vocabulary in AI because evaluation is fundamentally different from traditional software testing. Traditional software either works correctly or has a bug. AI systems operate on a spectrum of quality — they might be right 95 percent of the time but wrong in subtle, hard-to-predict ways the remaining 5 percent. Evals are designed to characterize this spectrum and help you decide whether the system is good enough for your use case.
How AI Evaluation Works
A comprehensive AI evaluation process typically includes several layers:
- Benchmark testing — Running the AI against standardized datasets with known correct answers to measure baseline performance. These benchmarks provide comparable metrics across different models and versions
- Domain-specific testing — Creating test sets that reflect your actual business use cases, terminology, and data patterns. Generic benchmarks often do not capture how well an AI performs on your particular tasks
- Adversarial testing (red teaming) — Deliberately trying to make the AI fail by feeding it tricky inputs, edge cases, ambiguous questions, and potentially harmful prompts. This reveals vulnerabilities before real users encounter them
- Bias and fairness evaluation — Testing whether the AI treats different demographic groups, languages, and cultural contexts equitably. This is especially important in diverse markets like Southeast Asia where AI must work across multiple languages and cultural norms
- Human evaluation — Having subject matter experts review AI outputs qualitatively, because some dimensions of quality such as tone, helpfulness, and appropriateness are difficult to measure automatically
- Production monitoring — Continuously evaluating AI performance after deployment using real user interactions, feedback, and outcome data. An AI that performs well in testing may behave differently when exposed to the unpredictable variety of real-world usage
Why AI Evaluation Matters for Business
Evals are not just a technical exercise — they directly protect your business:
Risk management. AI failures can range from embarrassing to catastrophic. A customer-facing chatbot that provides incorrect medical or financial advice could expose your company to legal liability. Evals help you identify and mitigate these risks before they reach users.
Regulatory compliance. AI regulations are expanding globally, and many require documented evidence that AI systems have been tested for accuracy, bias, and safety. Singapore's AI Verify framework, for example, provides a governance testing framework that organizations can use. Having a robust eval process is becoming a compliance requirement, not just a best practice.
Informed decision-making. Without evals, leaders are making AI investment and deployment decisions based on vendor claims and demos rather than evidence. Evals give you objective data about whether an AI system actually meets your standards before you commit to it at scale.
Continuous improvement. Evals are not a one-time gate. By running them continuously on deployed systems, you can detect performance degradation, identify new failure modes, and measure the impact of updates and improvements. This creates a virtuous cycle of ongoing quality improvement.
Vendor accountability. When evaluating AI vendors, your own evals give you an independent assessment rather than relying solely on the vendor's marketing materials. This is particularly valuable when comparing multiple options or negotiating contracts.
Key Examples and Use Cases
AI evaluation is relevant across every AI deployment scenario:
- Customer service AI — Testing chatbots and virtual agents against real customer queries to measure resolution rates, accuracy of information provided, tone appropriateness, and escalation effectiveness
- Content generation — Evaluating AI-written content for factual accuracy, brand voice consistency, cultural appropriateness across markets, and absence of harmful or biased content
- Credit and risk decisions — Testing AI lending and insurance models for accuracy across different demographic groups and ensuring compliance with fair lending regulations
- Healthcare AI — Rigorous clinical evaluation of diagnostic AI tools before deployment, including testing across diverse patient populations. In Southeast Asia, this includes ensuring models work accurately for local populations and medical conditions
- Multilingual AI — Evaluating performance parity across languages, which is critical in Southeast Asian markets where a single application might need to work in Bahasa Indonesia, Thai, Vietnamese, Tagalog, and English
Getting Started with AI Evaluation
Building an evaluation practice does not require massive investment. Start with these steps:
- Define what good looks like — Before testing, establish clear criteria for what constitutes acceptable performance for your specific use case. Include accuracy thresholds, response time requirements, and safety standards
- Build a golden test set — Create a curated collection of test inputs with verified correct outputs that represents your real-world use cases. Start with 50 to 100 examples and grow over time
- Automate where possible — Use automated evaluation tools to run your test suite regularly, especially before deploying updates. Manual review is important but does not scale
- Include diverse perspectives — Ensure your test data and human evaluators represent the diversity of your actual user base, including different languages, cultural contexts, and accessibility needs
- Make evals a deployment gate — Require that AI systems pass your evaluation criteria before they are deployed to production. This prevents the common mistake of shipping AI that has only been tested informally
- Track metrics over time — Monitor how evaluation scores change across versions and over time in production. Declining scores are an early warning that something needs attention
The Eval Culture Shift
One of the most important changes business leaders can drive is making evaluation a core part of AI culture, not an afterthought. The organizations that get the most value from AI are those that invest in rigorous, ongoing evaluation. This means allocating budget for eval infrastructure, ensuring product teams include evaluation in their development process, and making eval results visible to leadership.
Key Takeaways for Decision-Makers
- AI evaluation is as essential to AI deployment as quality assurance is to product launches
- Evals protect your business from accuracy failures, bias issues, regulatory non-compliance, and reputational damage
- Start with a domain-specific test set and automate evaluation as a required step before any AI deployment
- Make evaluation an ongoing practice, not a one-time gate, to catch performance degradation early
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- Establish clear evaluation criteria and golden test datasets specific to your business use cases before deploying any AI system to production
- Require AI evaluation as a mandatory deployment gate and allocate dedicated budget for evaluation tools and processes
- Ensure evaluation covers bias, fairness, and multilingual performance, especially when operating across diverse Southeast Asian markets
Frequently Asked Questions
How often should we run AI evaluations?
Run comprehensive evaluations before every major deployment or model update, and run lighter automated checks continuously in production. At minimum, schedule thorough evaluations quarterly even if no changes have been made, because AI performance can degrade over time as user behavior and data patterns shift. Think of it like financial auditing — regular checks prevent small issues from becoming major problems.
Can we rely on AI vendor benchmarks instead of running our own evals?
Vendor benchmarks are useful as a starting point but should never replace your own evaluations. Vendor benchmarks typically test general capabilities using standardized datasets, not your specific use cases, data, and requirements. An AI that scores well on generic benchmarks might perform poorly on your particular business tasks. Always supplement vendor metrics with domain-specific testing using your own data and success criteria.
More Questions
Red teaming is the practice of deliberately trying to make your AI system fail or produce harmful outputs by testing it with adversarial inputs, edge cases, and creative misuse scenarios. Yes, you need it if your AI interacts with customers or makes consequential decisions. Red teaming reveals vulnerabilities that standard testing misses. You can start with your own team brainstorming ways to break the system, and for higher-stakes applications, consider hiring specialized red teaming firms.
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