Explore a detailed step-by-step process on effectively evaluating AI systems to boost their potential.
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Artificial intelligence (AI) is fast becoming the defining technology of our time. A new UN Trade and Development (UNCTAD) report projects the global AI market will soar from $189 billion in 2023 to $4.8 trillion by 2033 – a 25-fold increase in just a decade.
But with this explosive growth comes heightened risk. 70% of AI initiatives stall once prototypes hit production, mainly because hidden errors stay invisible until customers notice. When that happens, you scramble through logs, guess at root causes, and hope the next deploy won't break something else.
Successful AI deployment demands more than sophisticated models; it requires systematic, end-to-end evaluation throughout the development lifecycle.
This guide presents a practical, step-by-step approach for AI evaluation, distilling best practices from production-scale deployments. Follow these methods to reduce deployment risk, meet emerging regulations like the EU AI Act, and demonstrate concrete ROI to executives.
We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies:
What is AI evaluation?
AI evaluation is the systematic assessment of artificial intelligence systems against performance benchmarks, business requirements, and ethical standards. Unlike traditional software testing, AI evaluation must address both deterministic functions and probabilistic behaviors that vary based on inputs, context, and even random factors.
Traditional machine learning relied primarily on quantitative metrics like accuracy, precision, and recall, measured against definitive ground truth.
Modern AI – especially generative systems – demands a multidimensional approach examining not just technical performance but also output quality, alignment with human values, and business impact.
Evolution of AI evaluation methods
AI evaluation has transformed dramatically alongside AI capabilities. Early approaches focused on simple classification metrics against labeled datasets. As AI systems grew more complex, evaluation expanded to include specialized benchmarks like GLUE and SuperGLUE for natural language understanding.
The emergence of large language models introduced new challenges, as these systems produce varied outputs that resist simple true/false assessment. Evaluation methods evolved to incorporate human feedback, model-based judgment, and multi-dimensional frameworks assessing factors from factual accuracy to ethical alignment.
Today's evaluation approaches increasingly integrate into development workflows rather than serving as final checkpoints.
This shift from post-hoc verification to continuous quality assurance reflects the understanding that robust AI systems require ongoing evaluation throughout their lifecycle – from initial design through deployment and beyond.
These evaluation advancements set the foundation for the structured step-by-step approach detailed in the following steps, guiding teams through implementing comprehensive AI evaluation frameworks.
AI evaluation step #1: Define clear success criteria with stakeholders
You've likely experienced the frustration of a technically sound AI model that fails to deliver business value. This disconnect often stems from misalignment between technical metrics and stakeholder expectations.
Engineering teams optimize for model accuracy while business leaders focus on ROI, regulatory compliance, and user satisfaction – creating a fundamental misalignment that dooms projects from the start.
Many organizations make the critical mistake of beginning evaluation after development, when it's too late to address fundamental design issues. Instead, successful evaluation begins before a single line of code is written, with structured stakeholder interviews capturing diverse perspectives.
Your interviews should include technical teams, business units, compliance officers, and executive sponsors to understand what "good" looks like from every angle.
Rather than vague aspirations, push stakeholders to articulate specific, measurable outcomes:
What business metrics should improve?
What user behaviors indicate success?
How will regulatory requirements be validated?
Document these criteria in a unified evaluation framework that clarifies priorities and identifies potential conflicts between objectives.
When competing priorities emerge, techniques like the Analytic Hierarchy Process can help quantify relative importance. This structured approach transforms subjective preferences into weighted criteria, creating transparency around inevitable trade-offs.
AI evaluation step #2: Build comprehensive test datasets
Your AI evaluation is only as good as the data powering it. Many enterprises fail by testing on artificial benchmarks that bear little resemblance to real-world conditions, resulting in models that perform brilliantly in the lab but collapse in production.
The more complex your AI system, the more critical diverse, representative test data becomes.
Building effective test datasets requires balancing several considerations. You need sufficient volume to ensure statistical validity, appropriate diversity to test across different conditions, and careful curation to avoid introducing bias.
Most organizations underestimate how much their training data differs from production environments, particularly when user behavior evolves over time.
The most common mistake is focusing exclusively on "happy path" scenarios while neglecting edge cases that often cause catastrophic failures. Your test datasets should deliberately include challenging inputs: ambiguous requests, out-of-distribution examples, adversarial inputs designed to confuse the model, and scenarios representing compliance boundaries.
For enterprises operating in regulated industries, test datasets must also reflect specific compliance requirements. Financial services firms need scenario testing for fair lending practices, while healthcare organizations must validate HIPAA compliance across diverse patient populations.
Galileo's Experiment functionality addresses these challenges through a systematic approach to test dataset management. This feature enables you to build, version, and deploy test datasets that mirror production conditions.
Rather than static collections, these datasets become living resources that evolve alongside your applications, with automatic versioning ensuring reproducible evaluations across development cycles.
AI evaluation step #3: Implement robust ground truth alternatives
When evaluating traditional ML models, you compare outputs against definitive correct answers. But generative AI presents a fundamental challenge: there's often no single "right" response to compare against.
How do you evaluate quality when ground truth is ambiguous or nonexistent? This challenge derails many evaluation efforts before they begin.
Organizations typically respond with makeshift approaches – relying on small samples of human judgments or using crude proxies like length or keyword matching. These methods prove inadequate at scale, introducing inconsistencies and failing to capture nuanced quality dimensions like reasoning or coherence.
A more effective approach leverages consensus methods, gathering judgments from multiple models or evaluators to approximate ground truth. This technique reduces individual biases but requires careful orchestration across evaluation sources.
Luna-2, Galileo's suite of Small Language Models (SLMs), delivers purpose-built evaluation at a fraction of the cost of traditional LLM-based approaches. With evaluation latency under 200ms and costs around $0.02 per million tokens (97% cheaper than GPT-4), Luna-2 makes comprehensive evaluation economically feasible even for large-scale enterprise deployments.
The multi-headed architecture enables hundreds of specialized metrics on shared infrastructure, providing ground truth alternatives across diverse evaluation dimensions.
AI evaluation step #4: Select metrics that matter for your use case
You face an overwhelming array of potential metrics when evaluating AI systems. Many teams make the critical mistake of tracking too many metrics without clear prioritization, creating noise that obscures meaningful insights.
Others focus exclusively on technical metrics like accuracy while neglecting business impact or user experience measures. Both approaches lead to misguided optimization.
Effective evaluation requires selecting metrics that align with your specific use case, risk profile, and business objectives. Technical performance forms just one dimension of a comprehensive framework that should also address relevance, safety, efficiency, and business impact.
Regulatory compliance adds another layer of complexity, particularly in industries like finance, healthcare, and insurance. Emerging frameworks like the EU AI Act demand specific evaluation approaches for high-risk applications, while sector-specific regulations impose additional requirements.
Galileo's comprehensive metrics address these challenges with purpose-built evaluators across five key categories:
Rather than generic metrics, these evaluators target specific AI behaviors. Further custom metrics capabilities let you define evaluation criteria using natural language descriptions, translating business requirements directly into measurable indicators.
AI evaluation step #5: Detect patterns and implement continuous improvement
You've collected evaluation data – now what? Many organizations struggle with the "metrics graveyard" problem: they gather volumes of evaluation results but fail to translate them into actionable improvements.
They might react to isolated incidents while missing systematic patterns, or identify issues without establishing clear processes for addressing them.
The root causes of AI failures typically extend beyond surface-level symptoms. When LLMs select incorrect tools or language models generate hallucinations, the underlying issues often involve complex interactions between prompt design, context handling, and model behavior.
Once patterns emerge, you need a structured approach to improvement. For minor issues, prompt engineering might suffice. More significant problems might require dataset augmentation, fine-tuning, or architectural changes.
However, without a prioritized improvement roadmap balancing quick wins with fundamental solutions, teams waste resources on superficial fixes.
Galileo's Insights Engine transforms evaluation from reactive monitoring to proactive improvement. This automated analysis system identifies failure patterns from logs and surfaces them with actionable root cause analyses.
Instead of spending hours scrolling through traces, your team receives clear, prioritized insights about tool errors, planning breakdowns, and other systematic issues.
AI evaluation step #6: Design effective guardrails for production safety
Your AI system performed well in testing, but what happens when it encounters unexpected inputs in production? Without proper guardrails, even well-designed systems can produce harmful, biased, or nonsensical outputs when facing real-world conditions.
Many organizations discover this vulnerability too late, after customer-facing failures damage trust and reputation.
Traditional approaches to AI safety rely heavily on human review, which doesn't scale with growing deployment volume. Other organizations implement crude filtering mechanisms that catch obvious issues but miss subtle problems like hallucinated information presented confidently.
Effective guardrails must balance restrictiveness with utility. Overly strict constraints limit functionality and frustrate users, while insufficient boundaries create safety risks. This calibration must be contextual – a customer service chatbot requires different protection than a medical advisor or financial system.
Many teams struggle to find this balance, either compromising safety or hobbling their AI's capabilities.
Galileo's runtime protection provides the industry's only runtime intervention capability with deterministic override/passthrough actions. Unlike passive monitoring tools that identify problems after they occur, runtime protection intercepts risky outputs before they reach users.
This real-time guardrail system blocks hallucinations, detects PII exposure, and prevents prompt injection attacks without adding significant latency. Each intervention generates comprehensive audit trails, providing documentation essential for regulatory compliance and continuous improvement.
AI evaluation step #7: Establish automated evaluation workflows
Point-in-time evaluations quickly become outdated as data distributions shift, user behavior evolves, and new edge cases emerge. Many organizations evaluate extensively during development but neglect ongoing monitoring, only discovering performance degradation after customer complaints or business impact.
By then, fixing issues becomes exponentially more expensive and damaging.
Manual evaluation processes create bottlenecks that slow innovation. Teams defer updates because evaluation takes too long, allowing competitors to move faster. Other organizations cut corners on evaluation to meet deadlines, introducing unnecessary risks. Neither approach proves sustainable for enterprise AI deployments.
The gap between development and production environments compounds these challenges. Systems evaluated in controlled settings behave differently under real-world conditions with unpredictable inputs, varying latency requirements, and integration complexities.
Without continuous evaluation spanning both environments, these divergences remain invisible until they cause failures.
Effective continuous evaluation requires integrating testing into your CI/CD pipelines, automating evaluation whenever code changes or models are retrained. Data drift detection systems should alert you when input distributions change significantly, triggering targeted evaluations.
Evaluate your AI systems and agents with Galileo
Implementing comprehensive AI evaluation creates a virtuous cycle – catching issues early reduces development costs, building stakeholder trust enables more ambitious projects, and documenting system performance satisfies regulatory requirements.
Galileo provides the infrastructure to make this systematic approach practical for enterprise teams:
Complete evaluation infrastructure for the entire AI lifecycle: Galileo supports every phase from development through production with specialized tools for each stage. Our platform enables systematic testing during development, comprehensive evaluation before deployment, and continuous monitoring in production.
Purpose-built metrics for complex AI systems: With specialized evaluators for agentic systems, RAG applications, and conversational AI, Galileo provides insights that traditional monitoring tools miss.
Enterprise-grade security and deployment flexibility: Whether you require on-premise deployment for sensitive applications or cloud-based monitoring for distributed systems, Galileo's SOC 2 compliance and enterprise-grade security ensure evaluation doesn't compromise system protection.
Scalable architecture proven at enterprise volume: Processing over 20 million traces daily while supporting 50,000+ live agents on a single platform, Galileo handles enterprise-scale evaluation without performance degradation.
Integration with your existing AI stack: Galileo works with any LLM provider, framework, or cloud, fitting seamlessly into your current architecture rather than requiring disruptive changes.
Intelligent failure detection: Galileo’s Insights Engine automatically clusters similar failures, surfaces root-cause patterns, and recommends fixes, reducing debugging time while building institutional knowledge
Human-in-the-loop optimization: Galileo's Continuous Learning via Human Feedback (CLHF) transforms expert reviews into reusable evaluators, accelerating iteration while maintaining quality standards
Get started with Galileo today to transform how your enterprise evaluates, improves, and governs AI systems throughout their lifecycle.