Deploying large language models (LLMs) without rigorous evaluation is risky: quality regressions, safety issues, and expensive rework often surface in production—when it’s hardest to fix. This guide translates Microsoft’s approach in Azure AI Foundry into a practical playbook: define metrics that matter (quality, safety, and business impact), choose the right evaluation mode (offline, online, human-in-the-loop, automated), and operationalize continuous evaluation with the Azure AI Evaluation SDK and monitoring. 

Quick-Start Checklist 

• Identify your use case: Match model type (SLM, LLM, task-specific) to business needs. 

• Benchmark models: Use Azure AI Foundry leaderboards for quality, safety, and performance, plus private datasets. 

• Evaluate with key metrics: Focus on relevance, coherence, factuality, completeness, safety, and business impact. 

• Combine offline & online evaluation: Test with curated datasets and monitor real-world performance. 

• Leverage manual & automated methods: Use human-in-the-loop for nuance, automated tools for scale. 

• Use private benchmarks: Evaluate with organization-specific data for best results. 

• Implement continuous monitoring: Set up alerts for drift, safety, and performance issues. 

Terminology Quick Reference 

• SLM: Small Language Model—compact, efficient models for latency/cost-sensitive tasks. 

• LLM: Large Language Model—broad capabilities, higher resource requirements. 

• MMLU: Multitask Language Understanding—academic benchmark for general knowledge. 

• HumanEval: Benchmark for code generation correctness. 

• BBH: BIG-Bench Hard—reasoning-heavy subset of BIG-Bench. 

• LLM-as-a-Judge: Using a language model to grade outputs using a rubric. 

The Generative AI Model Selection Challenge 

Deploying an advanced AI solution without thorough evaluation can lead to costly errors, loss of trust, and regulatory risks. LLMs now power critical business functions, but their unpredictable behavior makes robust evaluation essential. 

The Issue: Traditional evaluation methods fall short for LLMs, which are sensitive to prompt changes and can exhibit unexpected behaviors. Without a strong evaluation strategy, organizations risk unreliable or unsafe AI deployments. 

The Solution: Microsoft Azure AI Foundry provides a systematic approach to LLM evaluation, helping organizations reduce risk and realize business value. This guide shares proven techniques and best practices so you can confidently deploy AI and turn evaluation into a competitive advantage. 

LLMs and Use-Case Alignment 

When choosing an AI model, it’s important to match it to the specific job you need done. For example, some models are better at solving problems that require logical thinking or math—these are great for tasks that need careful analysis. Others are designed to write computer code, making them ideal for building software tools or helping programmers. There are also models that excel at having natural conversations, which is especially useful for customer service or support roles. Microsoft Azure AI Foundry helps with this by showing how different models perform in various categories, making it easier to pick the right one for your needs. 

 

Key Metrics: Quality, Safety, and Business Impact 

When evaluating an AI model, it’s important to look beyond just how well it performs. To truly understand if a model is ready for real-world use, we need to measure its quality, ensure it’s safe, and see how it impacts the business. Quality metrics show if the model gives accurate and useful answers. Safety metrics help us catch any harmful or biased content before it reaches users. Business impact metrics connect the model’s performance to what matters most—customer satisfaction, efficiency, and meeting important rules or standards. By tracking these key areas, organizations can build AI systems that are reliable, responsible, and valuable. 

Organizations that align model selection with use-case-specific benchmarks deploy faster and achieve higher user satisfaction than teams relying only on generic metrics. The key is matching evaluation criteria to business objectives from the earliest stages of model selection. 

Now that we know which metrics and parameters to evaluate LLMs on, when and how do we run these evaluations? Let’s get right into it.  

Evaluation Modalities 

Offline vs. Online Evaluation 

Offline Evaluation: Pre-deployment assessment using curated datasets and controlled environments. Enables reproducible testing, comprehensive coverage, and rapid iteration. However, it may miss real-world complexity. 
Online Evaluation: Assesses model performance on live production data. Enables real-world monitoring, drift detection, and user feedback integration.  

Best practice: use offline evaluation for development and gating, then online evaluation for continuous monitoring. 

Manual vs. Automated Evaluation 

Manual Evaluation: Human insight is irreplaceable for subjective qualities like creativity and cultural sensitivity. Azure AI Foundry supports human-in-the-loop evaluation via annotation queues and feedback systems. However, manual evaluation faces scalability and consistency challenges. 
Automated Evaluation: Azure AI Foundry’s built-in evaluators provide scalable, rigorous assessment of relevance, coherence, safety, and performance.  

Best practice: The most effective approach combines automated evaluation for broad coverage with targeted manual evaluation for nuanced assessment. Leading organizations implement a "human-in-the-loop" methodology where automated systems flag potential issues for human review.  

Public vs. Private Benchmarks 

Public Benchmarks (MMLU, HumanEval, BBH): Useful for standardized comparison but may not reflect your domain or business objectives. Risk of contamination and over-optimization. 
Private Benchmarks: Organization-specific data and metrics provide evaluation that directly reflects deployment scenarios.  
Best practice: Use public benchmarks to narrow candidates, then rely on private benchmarks for final decisions. 

LLM-as-a-Judge and Custom Evaluators 

LLM-as-a-Judge uses language models themselves to assess the quality of generated content. Azure AI Foundry’s implementation enables scalable, nuanced, and explainable evaluation—but requires careful validation. 

Common challenges and mitigations: 

• Position bias: Scores can skew toward the first-listed answer. Mitigate by randomizing order, evaluating both (A,B) and (B,A), and using majority voting across permutations. 

• Verbosity bias: Longer answers may be over-scored. Mitigate by enforcing concise-answer rubrics and normalizing by token count. 

• Inconsistency: Repeated runs can vary. Mitigate by aggregating over multiple runs and reporting confidence intervals. 

Custom Evaluators allow organizations to implement domain-specific logic and business rules, either as Python functions or prompt-based rubrics. This ensures evaluation aligns with your unique business outcomes. 

Evaluation SDK: Comprehensive Assessment Tools 

The Azure AI Evaluation SDK (azure-ai-evaluation) provides the technical foundation for systematic LLM assessment. The SDK's architecture enables both local development testing and cloud-scale evaluation: 

Cloud Evaluation for Scale: The SDK seamlessly transitions from local development to cloud-based evaluation for large-scale assessment. Cloud evaluation enables processing of massive datasets while integrating results into the Azure AI Foundry monitoring dashboard. 

Built-in Evaluator Library: The platform provides extensive pre-built evaluators covering quality metrics (coherence, fluency, relevance), safety metrics (toxicity, bias, fairness), and task-specific metrics (groundedness for RAG, code correctness for programming). Each evaluator has been validated against human judgment and continuously improved based on real-world usage. 

Real-World Workflow: From Model Selection to Continuous Monitoring 

Azure AI Foundry's integrated workflow guides organizations through the complete evaluation lifecycle: 

Stage 1: Model Selection and Benchmarking 

• Compare models using integrated leaderboards across quality, safety, cost, and performance dimensions 

• Evaluate top candidates using private datasets that reflect actual use cases 

• Generate comprehensive model cards documenting capabilities, limitations, and recommended use cases 

Stage 2: Pre-Deployment Evaluation 

• Systematic testing using Azure AI Evaluation SDK with built-in and custom evaluators 

• Safety assessment using AI Red Teaming Agent to identify vulnerabilities 

• Human-in-the-loop validation for business-critical applications 

Stage 3: Production Monitoring and Continuous Evaluation 

• Real-time monitoring through Azure Monitor Application Insights integration 

• Continuous evaluation at configurable sampling rates (e.g., 10 evaluations per hour) 

• Automated alerting for performance degradation, safety issues, or drift detection 

This workflow ensures that evaluation is not a one-time gate but an ongoing practice that maintains AI system quality and safety throughout the deployment lifecycle. 

Next Steps and Further Reading 

• Explore the Azure AI Foundry documentation for hands-on guides. 
• Find the Best Model - https://aka.ms/BestModelGenAISolution 
• Azure AI Foundry Evaluation SDK 

Summary  

Robust evaluation of large language models (LLMs) using systematic benchmarking and Azure AI Foundry tools is essential for building trustworthy, efficient, and business-aligned AI solutions 

Tags: #LLMEvaluation #AzureAIFoundry #AIModelSelection #Benchmarking #Skilled by MTT #MicrosoftLearn #MTTBloggingGroup