Contributors and Reviewers: Jay Sen (C), Anthony Nevico (C), Chris Kahrs (C), Anurag Karuparti (C), John De Havilland (R)

Key drivers behind GenAI Evaluation

• Risk Mitigation and Reliability: Proactively identifying issues to ensure GenAI models perform safely and consistently in critical environments. 


• Iterative Improvement: Leveraging continuous feedback loops and tests to refine models and maintain alignment with evolving business objectives. 


• Transparency and Accountability: Establishing clear, shared metrics that build trust between technical teams and business stakeholders, ensuring AI deployments are safe, ethical, and outcome driven. 


• Speed to market: Evaluation frameworks allow the adoption of GenAI technologies across business processes in a safe, sane and validated manner, allowing companies to innovate and iterate faster.


• Stay Ahead of Competition: Faster adoption of emerging technologies in AI with confidence, enabling quicker advancements and ensuring a competitive edge.

In this first episode of our blog series, we’ll explore how emerging practices in GenAIOps bridge industry leading GenAI technology with the rigor of enterprise-grade operations, a synergy that is redefining the future of cutting-edge AI integration.

What is GenAIOps ?

GenAIOps integrates people, processes, and platforms to enable the continuous delivery of value through generative AI applications. It focuses on evaluation, automation, and model lifecycle management to enhance decision-making, customer experiences, and innovation velocity. 

Many people with a DevOps background will find this approach familiar, as it builds on a strong foundation similar to DevOps.

GenAIOps Core Pillars: Build, customize, train, evaluate, and deploy AI responsibly with faster iterations

Why Evals are Crucial for Enterprise GenAI Development and Management?

The fast pace at which leading providers release and retire AI models presents major challenges for enterprises embedding GenAI into critical processes. Without strong evaluation practices, organizations risk inconsistent performance, misalignment with business goals, and loss of user trust.

Evals address key pain points

• Short Model Lifespans: Automated evaluations help organizations rapidly adapt to new models without extensive manual rework. 


• Trust and Alignment Challenges: Common evaluation frameworks foster shared understanding between SMEs, technical stakeholders, and end users


• High Manual Effort: Automation reduces the resource burden on SMEs, IT, and Data Science teams. 


• Delayed Feedback Loops: Accelerated evaluation enables faster iteration and time to value. 


• Inconsistent Methodologies: Standardized benchmarks improve reliability and comparability across models. 


• Siloed Development: Broad evaluation frameworks encourage reusable, scalable GenAI capabilities. 


• Variable Model Behavior: Comparative analysis surfaces differences between model versions and improves operational predictability.


• Non-Deterministic/ Probabilistic Behavior of AI models: Generative AI models have the potential to come up with multiple responses to a posed set of questions, including generating creative responses (e.g., hallucinations).  Evals frameworks help track model and prompt responses over time to ensure AI guardrails and structures are operating as intended.

Leveraging built-in evaluation frameworks—such as Azure AI Foundry Evaluations enterprises can automate comparisons across models and model versions, capturing insights into quality, consistency, and accuracy throughout the GenAIOps lifecycle.

By embedding evaluations early and often, businesses can:

• Align models tightly to evolving use cases and product developments


• Innovate faster to keep up the pace


• Build user trust and enhance safety at every step.


• Shape the future development of products and models through early actionable feedback.

Excited about the potential for Evals Frameworks?  Let’s kick the tires around how you’d get started:

When evaluating Generative AI across the enterprise lifecycle, break it down into three key stages: 

1. Selection of Base Model

Choosing the right foundation, such as GPT, Reasoning (o1, o3), LLaMA, or other open-source models, based on capabilities and business alignment.

How: Benchmarking tools and Model Leaderboard on Azure AI Foundry can help you make this decision, compare models across tasks (summarization, translation, etc.), industries, performance, cost, throughput.

2. Build an AI Application

Determine your data. Fine-tuning, prompt engineering, RAG, and integrations are addressed here. 

How:  Identify the data you’d like to leverage in your solution, vectorize and embed that data with Azure AI Search or other solutions, think about how you’ll leverage external data and tools to further enhance your context window, and tune your prompts

Tools: Azure AI Foundry Manual evaluation playground experience and automated evaluation using SDKs help you iterate and evaluate your AI applications faster with Business teams

Manual Evaluation: How to manually evaluate prompts in Azure AI Foundry portal playground – Azure AI Foundry | Microsoft Learn

Automated Evaluation: Local Evaluation with Azure AI Evaluation SDK – Azure AI Foundry | Microsoft Learn

3. Deploying the Product 

As the focus shifts towards production monitor performance data over time to evaluate model efficacy, overall performance, safety/sanity etc. Use this data to inform A/B tests in your lower environments. 

To help evaluate these stages consider these key elements supported by AI Foundry:

In-built Metrics

These are pre-validated by Microsoft’s Responsible AI Research team. Over 20 AI-assisted metrics help evaluate model behavior in terms of quality, NLP performance, safety, and risk. 

For example,

• Groundedness checks how well the answers are grounded on the provided data.


• Relevance measures how directly the answer addresses the user’s question using context. 


• Coherence ensures the response flows logically, is easy to follow, and matches user expectations. 


• Fluency and Similarity assess grammar and language quality (fluency) and how closely the AI output matches a known correct answer (similarity).


• For a more detailed description refer to these documentations: Monitoring evaluation metrics descriptions and use cases (preview) – Azure Machine Learning | Microsoft Learn

Custom Metrics

Enterprises should define metrics specific to their domain or use case – think tone (e.g. friendliness) in marketing copy.

Below we have mentioned a few metrics/evals – in-built and custom available on Azure AI Foundry to evaluate your AI Applications. for a full list, we recommend looking into this resource.

Evaluation Spectrum

It’s essential to test across a diverse set of six datasets, simulating real-world variability and edge cases. 

When building and refining generative AI applications, selecting the right evaluation dataset is critical. Each dataset type serves a different purpose—whether it’s testing for quality, robustness, safety, or real-world performance. Here’s a simple breakdown of commonly used evaluation datasets:

• Qualified Answers: Expert-generated examples used to assess core response quality (e.g., relevance, coherence, groundedness). 


• Synthetic: Large-scale, generated examples that stress-test, quality of responses, retrieval and tool-use capabilities. 


• Adversarial: Designed to catch unsafe behavior such as jailbreaks or harmful content generation. 


• OOD (Out-of-Domain): Questions that fall outside a model’s expected knowledge area, used to test control over hallucination and improve fallback behavior (“I don’t know”), prevent overfitting.


• Thumbs Down: Real examples where users rated answers poorly to identify failure modes. This information can be obtained from the Azure AI Foundry Playground that provides evaluation capabilities.


• PROD: Scrubbed, anonymized production queries to ensure the model meets user satisfaction at scale. 

These datasets are used together to fine-tune models across key dimensions like safety, relevance, and user trust.

Tools: Azure AI offers tools to generate synthetic data using its SDK.:How to generate synthetic and simulated data for evaluation – Azure AI Foundry | Microsoft Learn

Experimentation

Azure AI Foundry Playground provides a hands-on environment for teams to explore and evaluate model behaviors under different scenarios.

A/B experimentation is essential in AI application development as it enables continuous, data-driven evaluation of models and features. It helps compare different versions to optimize performance, reduce bias, and improve fairness across user groups. This method accelerates innovation by quickly validating new ideas and refining features. It also enhances user experience and supports more informed, cost-effective decision making.

Learn more about A/B Experimentation here: A/B experiments for AI applications – Azure AI Foundry | Microsoft Learn

For A/B testing, Microsoft will be releasing new out of the box tools (later this year) to make it easy for our customers.

Learn more about the manual testing playground here: How to manually evaluate prompts in Azure AI Foundry portal playground – Azure AI Foundry | Microsoft Learn

Automation

Use a code-first approach with Azure AI Evaluations SDKs, GitHub Actions, and CI/CD pipelines to integrate evaluation into the GenAIOps workflow.

The Azure AI Evaluation SDK helps you measure the performance of your generative AI app using test data. It uses both mathematical metrics and AI-assisted evaluators for quality and safety. You can run evaluations locally or in the cloud and track results in Azure AI Studio.

Learn more about Azure AI Evaluation SDK – Local Evaluation with Azure AI Evaluation SDK – Azure AI Foundry | Microsoft Learn

This is a sample Github repository that uses this SDK to evaluate responses with in-built and custom evaluators.anuragsirish/genai-evals: Evaluating Gen AI responses

By aligning these five principles with each stage of the GenAI lifecycle, organizations can ensure a rigorous, responsible, and scalable deployment of GenAI systems.

Wrapping up

Aligning the latest AI innovations with automated GenAIOps and evaluations tailored to specific use cases can significantly enhance organizational value and accelerate business outcomes.