AI Model Evaluation Guide: Methods, Metrics, and Why It Determines Production Success

Introduction

Most AI teams spend 90% of their effort on model architecture, training data, and hyperparameter tuning — and 10% on evaluation. If the failure rates are any indication, that ratio should be closer to 50/50.

Here is a number worth sitting with: more than 80% of AI projects fail, according to RAND Corporation research — roughly twice the failure rate of non-AI IT projects. The root cause, consistently, is not bad algorithms or insufficient compute. It is bad data foundations. And within that category, one of the most underestimated contributors is the quality of evaluation itself.

The gap between what AI models can do in a demo and what they can do in production is an evaluation gap. Teams that treat evaluation as a checkbox — run the benchmark, check the leaderboard, ship it — are the teams most likely to end up in that 80%. Meanwhile, Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by end of 2025, citing poor data quality and inadequate risk controls as primary causes.

AI model evaluation is the systematic process of assessing whether an AI system — a large language model, a vision model, an agentic workflow — is accurate, safe, fair, and fit for its intended use. It encompasses everything from automated benchmark testing to human expert review to adversarial red teaming. Evaluations are ultimately how organizations determine whether a model is ready to deploy, safe enough to use, or worth further investment. And evaluation has become the critical bottleneck in the AI deployment pipeline, not because teams don't want to evaluate, but because evaluating AI models well is genuinely hard.

The challenge is that evaluation quality is itself a data quality problem. Your evaluation is only as reliable as the rubrics that define "good," the reference answers that set the standard, and the human judgments that calibrate the signal. Get those wrong, and even a sophisticated evaluation pipeline will produce misleading results — the kind that lead to confident deployment decisions followed by production failures.

This guide walks through the evaluation landscape as it stands today, explains when each method works and when it breaks down, and argues that the teams gaining a competitive advantage are the ones investing in evaluation rigor — not just model sophistication.

What Is AI Model Evaluation and Why Does It Matter?

AI model evaluation is the discipline of measuring whether a machine learning model does what you need it to do — not in the abstract, but in the specific context where you intend to deploy it. That distinction matters more than most teams realize.

Stanford's AI Index report in 2025 showed that 78% of organizations use AI in at least one business function, a stark difference from 55% in 2024. Adoption is accelerating. But the same report notes that standardized responsible AI evaluations remain rare among major model developers. The industry is deploying AI models faster than it is evaluating them.

This creates a predictable pattern. An MIT study on enterprise AI found that roughly 95% of enterprise AI pilots fail to deliver meaningful ROI. The problem is not usually model capability — it is the gap between what was tested and what the production environment actually demands. For example, a machine learning model that scores well on a benchmark can still fail on the specific distribution of queries your users generate, the edge cases your domain produces, or the safety constraints your industry requires.

The teams that treat evaluation as a first-class engineering discipline — allocating time, budget, and domain expertise — are the ones that consistently bridge the gap from prototype to production. This is not a minor procedural detail. It is an increasingly recognized competitive advantage in evaluating AI systems for real world applications.

What Are the Main AI Evaluation Methods?

There is no single evaluation method that covers everything. The right evaluation metrics and methods depend on your task type, your deployment context, and the risk profile of your application. The question is not "which method should I use?" but "which combination of methods gives me the signal I need?" Here is how to evaluate AI models using the four primary approaches — each suited to a different task type — plus the hybrid pipelines that increasingly represent best practice for evaluating AI systems end-to-end.

Benchmarks (Automated Evaluation)

Benchmarks test models against standardized test data with known correct answers. They are scalable, reproducible, and cheap to run — which is why they remain the default starting point for assessing performance across different models. Stanford's HELM framework measures seven dimensions (accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency) across dozens of scenarios, representing the most comprehensive attempt at holistic benchmarking.

But benchmarks have structural limitations that are growing more severe. More on that in the next section.

When to use: Initial model screening, regression testing, CI/CD gating, comparing different models on a shared baseline. For example, benchmarks measure performance on a fixed set of tasks — they do not tell you how a model performs on your specific task in real world conditions.

LLM-as-a-Judge

LLM-as-a-judge uses one language model to evaluate the model outputs of another. A comprehensive survey identifies four core approaches: score generation (rating on a numerical scale), binary classification (yes/no judgments), pairwise comparison (choosing the better of two outputs), and multiple-choice selection. Of these, pairwise comparison has shown the most consistent evaluation results, making it the preferred method for evaluating open-ended model outputs.

The appeal is clear: using an LLM as a judge is faster and cheaper than human evaluation, and it can handle open-ended outputs that benchmarks cannot score. LLM evaluations can also be tailored to specific tasks, making them adaptable across different task types and AI applications. But this approach comes with significant biases. Research documenting 12 key biases found that position bias — favoring responses based on their order in the prompt — worsens significantly when evaluating three or four candidates rather than two. Verbosity bias causes LLM judges to prefer longer responses regardless of actual quality. And self-enhancement bias means models tend to rate their own model outputs more favorably than equivalent outputs from other models.

A separate study testing 15 LLM judges across 150,000+ evaluation instances confirmed that position bias is a structural problem, not an occasional artifact. The practical implication: LLM-as-a-judge is a valuable tool for scaling evaluations, but its outputs need human calibration to be trustworthy. LLM-based evaluations should be validated against human benchmarks to ensure reliability before being used at scale. Without a ground truth layer produced by calibrated human evaluators, LLM judge scores can systematically mislead.

When to use: Scaling evaluation of open-ended model outputs, pre-screening before human review, rapid iteration during development. For example, teams evaluating AI chatbot models might use LLM-as-a-judge to score thousands of responses daily, then route a sample to domain experts for calibration. Always pair with human evaluation for final quality signals.

Human Expert Evaluation

Human expert evaluation remains the gold standard for tasks where correctness is subjective, domain-specific, or safety-critical. No automated method reliably captures whether a medical AI model's recommendation is clinically sound, whether a legal summary preserves the relevant nuance, or whether a conversational agent's response would be harmful in a sensitive context. Automated evaluation metrics can overlook nuances in tone, creativity, and contextual appropriateness — which is precisely where human review adds the most value.

The challenge is making human evaluation reliable rather than just expensive. This requires calibrated annotators, well-designed rubrics, and systematic monitoring of inter-annotator agreement — all of which are data quality problems. Research on rubric-based evaluation demonstrates that analytic rubrics breaking quality into multiple evaluation criteria produce more reliable signals than holistic scoring. Structured human evaluation — using rubric-based scoring, pairwise comparisons, and targeted review of failure-prone slices — scales more effectively than unstructured review of every model output. Many teams focus human review on targeted slices of model outputs to surface failure modes and guide improvements without reviewing every response.

When to use: Safety-critical applications, domain-specific correctness, subjective quality assessment, final quality gate before deployment. Domain experts are irreplaceable for complex tasks where the model's output matches (or fails to match) real-world professional standards.

Red Teaming

Red teaming involves deliberately trying to make an AI system fail, produce harmful outputs, or violate its safety constraints. It is adversarial by design — the goal is to surface failure modes that standard evaluation would miss.

In 2025, Anthropic and OpenAI conducted a joint alignment evaluation exercise, evaluating each other's public models for propensities related to sycophancy, whistleblowing, and self-preservation, using human raters for ambiguous contexts. This represents an emerging model for cross-organizational safety evaluation.

When to use: Pre-deployment safety assessment, regulatory compliance, identifying adversarial vulnerabilities, testing alignment and refusal behaviors. Red teaming is especially critical for evaluating AI models deployed in high-stakes real world applications where failure modes may not be apparent through standard metrics.

Hybrid Pipelines

In practice, production-grade evaluation combines all four approaches. Benchmarks handle initial screening and regression testing. LLM-as-a-judge scales evaluation of open-ended model outputs. Human experts provide the calibration layer and final quality gate. Red teaming surfaces what the other methods miss. A hybrid evaluation strategy that combines automated metrics with periodic human reviews balances cost, scale, and accuracy more effectively than either approach alone.

The hybrid approach demands the most from evaluation infrastructure — and from evaluation data quality — but it produces the most comprehensive and trustworthy signal for measuring model performance across different task types.

Three Metrics That Matter Beyond Accuracy

Standard metrics like accuracy, precision, and recall are table stakes. Common metrics such as these measure performance on structured prediction tasks, but the evaluation metrics that separate rigorous evaluation from checkbox evaluation target what actually breaks in production. For classification models, the F1-Score — the harmonic mean of precision and recall — remains important for imbalanced datasets. But for large language models and generative AI systems, you need metrics designed for open-ended generation.

Faithfulness measures whether the model's output is factually grounded in the provided context — critical for any retrieval augmented generation system or retrieval-based pipeline. Retrieval augmented generation (RAG) models are especially prone to hallucination, and faithfulness is the metric designed to catch it. It works by extracting claims from the generated answer and verifying each one against the source context, scored 0–1. Beyond faithfulness, LLM evaluation increasingly relies on measures like answer correctness, semantic similarity, and hallucination rate to quantify how well language models perform across different task types. (RAGAS framework)

Pass@k / Reliable@k measures consistency rather than peak performance. Pass@k asks whether a system succeeds at least once in k attempts; reliable@k asks whether it succeeds every time across k variant prompts. The distinction matters: a system that passes 60% on a single run but only 25% across eight consecutive runs is unreliable regardless of its accuracy score. This is especially important for agentic AI evaluation, where research found 50x cost variations between AI agents achieving comparable accuracy.

Prompt Sensitivity is a meta-evaluation: inject perturbations — case-swapping, whitespace, paraphrasing — into inputs and measure how much model outputs degrade. A Google research team recommends this as a standard evaluation step, because a machine learning model that scores 90% on clean inputs but 60% on lightly perturbed inputs is fragile in production, no matter what the benchmarks say. Evaluation metrics can include both quantitative measurements like these and qualitative assessments through human review — and the most effective evaluation strategies use both.

Why Are Traditional Benchmarks No Longer Enough?

The benchmark crisis is not a future concern; it is a present reality. An ICML 2025 study systematically analyzed 60 AI benchmarks and found that 29 — nearly half — exhibit high or very high saturation. These benchmarks can no longer meaningfully distinguish between top-performing AI models. Saturation worsens with age, rising from 42.9% for recent benchmarks to 54.5% for those older than 60 months. And the assumed safeguards don't help: private test sets, open-ended formats, and multilingual designs showed no reliable protection against saturation.

The problems go deeper than saturation. A European Commission JRC interdisciplinary review catalogued nine systemic issues with AI benchmarks: data collection gaps, weak construct validity, sociocultural neglect, narrow diversity, commercial incentive distortion, gaming and measure corruption, dubious community vetting, rapid saturation, and unknown unknowns. Over 70% of computer vision benchmarks reuse datasets from other domains, and only 13% of analyzed models reported train-test overlap. The review's blunt conclusion: "no benchmark is neutral."

Then there is Goodhart's Law — when a measure becomes a target, it ceases to be a good measure. AI leaderboard gaming is now well-documented. Large companies can privately test multiple model variants and only publish the best evaluation results. Models have been found to be optimized for answering the multiple-choice questions that dominate benchmarks, rather than for the open-ended tasks that dominate real-world use.

The implication for AI practitioners is clear: benchmarks are necessary for baseline comparison, but they are nowhere near sufficient for deployment decisions. Testing AI models against real-world data, rather than relying solely on academic datasets, is what ensures evaluation results translate to production effectiveness. Teams that rely on leaderboard scores as their primary evaluation signal are making decisions on degraded instruments. Domain-specific evaluation, built on custom test data and expert human judgment, is what bridges the gap between benchmark performance and real world performance.

How Do You Evaluate AI Agents?

AI agent evaluation is the fastest-growing subfield in evaluation research, and for good reason. Agentic AI systems — multi-step, tool-using workflows that interact with real environments — break most of the assumptions that traditional LLM evaluation relies on.

The differences are structural. A survey of agent architectures and evaluation identifies the core challenge: traditional evaluation measures single-response quality, but AI agents operate over multi-step sequences where small deviations in early steps can cascade into unseen states with no recovery mechanism. Add non-determinism from sampling and tool variability — including unpredictable tool calls and external API responses — and you have systems that are fundamentally harder to evaluate reproducibly.

The numbers make this concrete. Research on enterprise agentic AI evaluation found cost variations of 50x for AI agents achieving comparable accuracy levels. Even more striking: reliability drops from 60% on a single run to just 25% when measured across eight consecutive runs. An agent that passes a one-shot evaluation may fail three out of four times in sustained operation.

The CLEAR framework proposes five agentic ai evaluation metrics that go well beyond traditional accuracy:

• Cost (economic efficiency),
• Latency (response time under SLA constraints),
• Efficacy (task completion quality),
• Assurance (safety, security, policy compliance), and
• Reliability (consistency via pass@k metrics).

For evaluating AI agents, all five dimensions matter — and each one requires its own evaluation data, rubrics, and success criteria.

What does this mean in practice? Evaluating an AI agent that handles customer support tickets is not like evaluating conversational agents that answer single questions. You need rubrics for each decision point in the workflow: did the agent correctly classify the issue, select the right tool calls, execute the right sequence of actions, handle the edge cases gracefully, and know when to escalate to a human? Each of these task completion checkpoints is an evaluation data problem — and the quality of those rubrics determines whether your evaluation catches the failure modes that will surface in production.

Why Does Evaluation Quality Depend on Data Quality?

This is the question at the center of the entire evaluation discipline, and the answer is deceptively simple: your evaluation is only as good as the data that produces your evaluation signal.

Consider what happens when evaluation data is flawed. If your benchmark test data contains errors or doesn't represent your production distribution, your accuracy metrics are misleading. If your LLM-as-a-judge rubric is vague, your automated scores will be noisy. If your human evaluators aren't calibrated, their disagreements will look like model inconsistency rather than annotator inconsistency. This matters because the evaluation process feeds directly into deployment decisions — and flawed evaluation data means every decision downstream is built on unreliable ground.

Research on inter-annotator agreement makes this point precisely: high agreement between annotators can actually obscure flawed or superficial annotations, while disagreement may indicate productive ambiguity that deserves attention rather than suppression. The recommendation is to weight or calibrate annotators rather than assuming uniform reliability — because annotator quality directly determines evaluation signal quality.

This creates what might be called the evaluation-data quality chain: evaluation data quality determines evaluation signal quality, which determines deployment decision quality. Break any link in that chain, and you get the pattern that RAND, Gartner, and MIT all document — confident deployment decisions followed by production failure.

The practical implications are specific — and rubric design illustrates them well. The Autorubric framework (University of Pennsylvania, 2025) codifies what makes evaluation rubrics reliable. A well-designed rubric is analytic (each criterion scored independently, preventing halo effects), differentially weighted (factual correctness weighted higher than tone), and behaviorally anchored (each scoring level describes observable behavior like "fully resolves with actionable steps," not vague labels like "good"). Research consistently shows that rubrics with behaviorally-anchored levels produce substantially higher inter-annotator agreement than vague ordinal scales — because the rubric constrains what "good" means, reducing subjective interpretation.

Beyond rubrics, reliable evaluation requires reference answers that reflect your actual production context, not generic samples. It requires human evaluators with domain expertise who are calibrated against each other and against a consistent standard. And it requires monitoring — because training data distributions can shift, evaluation data quality can drift, and model performance can degrade in ways that only continuous measurement catches. When training data evolves after deployment, previously valid evaluation benchmarks may no longer measure performance against what the model actually encounters in production.

The most effective evaluation strategies are iterative, contextual, and grounded in the specific realities of your domain — not generic playbooks applied uniformly across use cases. Organizations that invest in evaluation data quality — treating rubric design, annotator calibration, and reference data curation as engineering problems rather than administrative tasks — are the ones that build evaluation pipelines they can actually trust. This is why teams increasingly turn to partners like Kili Technology that combine verified domain experts with data science-led evaluation design: the quality of human review, the rigor of rubric frameworks, and the consistency of annotator calibration are what make evaluation signals trustworthy at scale.

What Are the Regulatory Requirements for AI Model Evaluation?

Evaluation rigor is no longer just best practice — it is becoming a legal obligation for organizations deploying AI models in regulated industries. Two frameworks are driving this shift.

The EU AI Act requires a continuous, iterative risk management system throughout the entire lifecycle of high-risk AI systems. Article 9 mandates identification of foreseeable risks and adoption of targeted risk management measures. Article 15 goes further: high-risk AI systems must achieve "an appropriate level of accuracy, robustness, and cybersecurity," and — critically — the levels of accuracy and relevant accuracy metrics must be declared in the accompanying instructions of use. This means organizations deploying high-risk AI models in the EU will need documented, auditable evaluation processes with specific accuracy claims backed by robust evaluation data.

The NIST AI Risk Management Framework organizes AI governance into four functions: Govern, Map, Measure, and Manage. The Measure function is explicitly about evaluation — and NIST's companion documents, expanded through 2024–2025, provide increasingly specific guidance on multi-dimensional metrics that go beyond accuracy to measure performance across fairness, robustness, and continuous monitoring.

These aren't abstract compliance exercises. The EU AI Act applies to any organization deploying high-risk artificial intelligence that serves EU citizens, regardless of where the organization is headquartered. The NIST framework, while voluntary in the US, is rapidly becoming the de facto standard that auditors and procurement teams reference. Organizations that cannot demonstrate systematic evaluation processes — with documented rubrics, tracked evaluation metrics, and auditable human review — will face increasing friction in regulated industries.

Where Is AI Evaluation Heading?

The evaluation landscape is moving in three directions simultaneously.

First, continuous evaluation is replacing point-in-time testing. The traditional pattern — evaluate once before deployment, then monitor loosely — is giving way to evaluation pipelines that run continuously, catching model performance drift, distribution shift, and emerging failure modes in real time. This is especially critical for machine learning models and neural networks that learn after deployment, where feedback loops can introduce new biases that only surface over time. Data-centric approaches to evaluation promote rapid iteration: better evaluation data enables faster identification of failure modes, which in turn guides targeted model improvements.

Second, hybrid human-automated pipelines are becoming the standard architecture for serious evaluation. The Anthropic-OpenAI joint evaluation exercise is an early signal of this trend: even frontier AI labs are using human raters for ambiguous contexts and combining automated assessments with expert judgment. The question is no longer whether to use LLM-as-a-judge alongside human-in-the-loop workflows — it is how to orchestrate the combination efficiently to evaluate models at scale while maintaining the quality of human review.

Third, evaluation-as-a-discipline is emerging as a distinct capability rather than a step in the ML pipeline. The Stanford AI Index notes that responsible AI evaluations remain rare even among major model developers. But the regulatory environment, the documented failure rates, and the growing complexity of agentic AI systems are all pushing evaluation toward professionalization — with dedicated teams, specialized tools, and systematic data infrastructure to gain insights into how AI models actually perform under real world conditions.

Underpinning all three trends is what might be called the evaluation-data quality flywheel. Better evaluation data produces more reliable evaluation signals. More reliable signals produce better deployment decisions. Better deployment outcomes justify further investment in evaluation infrastructure — which starts with better evaluation data. The organizations that enter this flywheel early build a compounding advantage: they not only evaluate better today, they get better at evaluating over time.

The common thread is data quality. Continuous evaluation requires continuously maintained training data and evaluation data. Hybrid pipelines require calibrated human judgment from domain experts who can measure performance against domain-specific standards. Evaluation as a discipline requires institutional knowledge about what "good" looks like for a given task in your specific domain — encoded in rubrics, reference data, and annotator expertise.

The organizations that will evaluate well are the ones that treat evaluation data as a first-class asset, not a byproduct of the deployment process.

What Reliable AI Evaluation Actually Requires

AI model evaluation is the discipline that determines whether an AI system is ready for the real world — not the benchmark world, not the demo world, but the world where your users, your regulators, and your business depend on it performing correctly.

The evidence is consistent: most AI projects fail, benchmarks are saturating, and the gap between lab performance and production performance is an evaluation gap. Closing that gap requires moving beyond generic benchmarks to domain-specific evaluation built on reliable data — calibrated human judgments, well-designed rubrics, and hybrid pipelines that combine the scale of automation with the rigor of human expertise. The choice of evaluation methods should evolve alongside the models they measure.

Evaluation quality is not a nice-to-have. It is the mechanism through which organizations turn AI capability into AI reliability. And it starts with the quality of the data that produces your evaluation signal — the domain experts who review model outputs, the rubrics that define what "correct" means for your specific task, and the infrastructure that keeps evaluation consistent as models and production conditions evolve.

If you're building an evaluation pipeline — or questioning whether your current one is giving you trustworthy signals — explore our guide to choosing an AI model evaluation service for a structured framework.

Resources

Academic Research on Benchmarks and Evaluation

• When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation – ICML 2025 study finding nearly half of 60 benchmarks are saturated
  • https://arxiv.org/html/2602.16763v1
• Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation – EU JRC review identifying 9 systemic benchmark issues
  • https://arxiv.org/html/2502.06559v1
• Holistic Evaluation of Language Models (HELM) – Stanford CRFM's multi-dimensional evaluation framework
  • https://arxiv.org/abs/2211.09110

LLM-as-a-Judge Research

• Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge – Documents 12 key biases in LLM judge systems
  • https://arxiv.org/abs/2410.02736
• Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge – 150K+ instance study of position bias across 15 LLM judges
  • https://arxiv.org/abs/2406.07791
• A Survey on LLM-as-a-Judge – Comprehensive taxonomy of LLM-as-judge methods and limitations
  • https://arxiv.org/abs/2411.15594

Agentic AI Evaluation

• AI Agent Systems: Architectures, Applications, and Evaluation – Survey on agent evaluation challenges including compounding errors
  • https://arxiv.org/html/2601.01743v1
• Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems – CLEAR framework for enterprise agent evaluation
  • https://arxiv.org/html/2511.14136v1

Human Evaluation and Data Quality

• Counting on Consensus: Selecting the Right Inter-annotator Agreement Metric – Research on annotator calibration and IAA methodology
  • https://arxiv.org/html/2603.06865
• RUBICON: Rubric-Based Evaluation of Domain-Specific Human AI Conversations – Microsoft Research framework for rubric-based evaluation
  • https://www.microsoft.com/en-us/research/wp-content/uploads/2024/05/RUBICON__Rubric_Based_Evaluation_of_Domain_Specific_Human_AI_Conversations-10.pdf

Enterprise and Market Data

• The Root Causes of Failure for Artificial Intelligence Projects – RAND Corporation analysis of 80%+ AI project failure rate
  • https://www.rand.org/pubs/research_reports/RRA2680-1.html
• Gartner: 30% of GenAI Projects Abandoned After POC – Analyst prediction on GenAI project abandonment
  • https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025
• Stanford AI Index Report 2025 – Enterprise AI adoption data and evaluation gap analysis
  • https://hai.stanford.edu/ai-index/2025-ai-index-report
• The GenAI Divide: State of AI in Business 2025 – MIT study on enterprise AI pilot failure rates
  • https://www.technologyreview.com/2025/06/24/1119187/fix-ai-evaluation-crisis/

Safety and Alignment Evaluation

• Findings from a Pilot Anthropic–OpenAI Alignment Evaluation Exercise – Joint cross-organizational safety evaluation precedent
  • https://alignment.anthropic.com/2025/openai-findings/

Evaluation Metrics and Rubric Design

• RAGAS: Available Metrics – Faithfulness, answer relevancy, and context relevancy metrics for RAG evaluation
  • https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/
• A Practical Guide for Evaluating LLMs and LLM-Reliant Systems – Google research team's three-pillar evaluation framework including prompt sensitivity testing
  • https://arxiv.org/html/2506.13023v1
• Autorubric: A Unified Framework for Rubric-Based LLM Evaluation – University of Pennsylvania framework for analytic rubric design
  • https://arxiv.org/html/2603.00077v1

Kili Technology Resources

• Guide: How to Choose an AI Model Evaluation Service in 2026 – Framework for selecting an evaluation partner
  • https://kili-technology.com/blog/guide-how-to-choose-an-ai-model-evaluation-service-in-2026
• Keys to Successful LLM-as-a-Judge and HITL Workflows – Practical guidance on hybrid evaluation pipelines
  • https://kili-technology.com/blog/keys-to-successful-llm-as-a-judge-and-hitl-workflows

Regulatory Frameworks

• EU AI Act — Article 9: Risk Management System – Mandatory evaluation requirements for high-risk AI
  • https://artificialintelligenceact.eu/article/9/
• EU AI Act — Article 15: Accuracy, Robustness and Cybersecurity – Mandatory accuracy declaration requirements
  • https://artificialintelligenceact.eu/article/15/
• NIST AI Risk Management Framework (AI RMF 1.0) – Govern, Map, Measure, Manage framework for AI evaluation
  • https://www.nist.gov/itl/ai-risk-management-framework

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