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Evaluating generative AI: Best practices for developers

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As a developer using generative AI, you may be amazed at the impressive results your models can produce. But how can you ensure that these outputs consistently meet quality standards and business requirements? Enter the essential world of generative AI evaluation!

Why evaluations are important

Evaluating generative AI results is not just a best practice; it is essential to building robust and reliable applications. Here’s why:

  1. quality assurance: Make sure AI-generated content meets your standards.
  2. Performance Tracking: Helps you monitor and improve app performance over time.
  3. user trust: Build trust in AI applications among end users.
  4. regulatory compliance: Helps meet new AI governance requirements.

Best Practices for Generative AI Assessment

In the rapidly evolving field of generative AI, ensuring the reliability and quality of app output is of utmost importance. As developers, we strive to create applications that not only amaze users with their capabilities, but also maintain a high level of trust and integrity. Achieving this requires a systematic approach to evaluating AI systems. Let’s look at some best practices for evaluating generative AI.

Clear indicator definitions

Establishing clear metrics serves as a cornerstone for evaluating the effectiveness and stability of your app. Without well-defined criteria, the evaluation process can be subjective and inconsistent, leading to incorrect conclusions. Clear metrics translate the abstract concept of “quality” into practical, measurable goals, providing a structured framework to guide both development and iteration. This clarity is critical to aligning results with business goals and user expectations.

Context is key

Always evaluate output in the context of the intended use case. For example, generative AI used in a creative writing app may prioritize originality and narrative flow, but these same criteria are inappropriate for evaluating a customer support app. The primary metrics here focus on accuracy and relevance to user queries. The context in which AI operates fundamentally changes the evaluation framework, requiring customized criteria that match the specific goals of the application and user expectations. Understanding context therefore ensures that the evaluation process is relevant and rigorous and provides meaningful insights that drive improvement.

Take a multi-pronged approach

Relying on a single method to evaluate generative AI can lead to an incomplete and potentially distorted understanding of its performance. Adopting a multifaceted approach can leverage the strengths of a variety of assessment techniques to provide a more holistic view of the capabilities and limitations of AI. This comprehensive strategy combines quantitative and qualitative assessments to capture a broader range of performance indicators.

Quantitative metrics like Perplexity and BLEU score provide objective, repeatable measurements that are essential for tracking improvements over time. However, these metrics alone often fail to capture the nuanced requirements of real-world applications. This is where qualitative methods, including expert reviews and user feedback, come into play. These methods add a layer of human judgment by considering context and subjective experience that automated metrics may miss.

Conduct continuous evaluation

The efficiency and stability of your application are not static metrics. Regular and ongoing scrutiny is required to ensure that the high standards set during development are consistently met. Continuous evaluation is therefore essential because it allows developers to identify and fix problems in real time, allowing AI systems to adapt to new data and evolving user needs. This approach fosters a proactive attitude, enabling rapid improvements and maintaining end-user trust and satisfaction.

Frequent and scheduled evaluations should be part of the development cycle. Ideally, evaluations should be performed whenever there is a significant iteration or update to an AI model or system prompt. Additionally, periodic evaluations, monthly or quarterly, can help track the long-term performance and stability of your AI system. Maintaining this rhythm allows developers to quickly respond to quality declines to keep their applications robust and aligned with their intended goals.

Don’t treat assessments as a one-off task! Set up a system for continuous monitoring and feedback loops.

Learn more with our new assessment. learning path

We are pleased to announce new news. learning path Designed to help you take your assessment skills to the next level!

Module 1: Evaluating generative AI applications

In this module, you will learn the basic concepts of evaluating generative AI applications. This module is a great starting point for anyone new to evaluation in the context of generative AI. This module explores the following topics:

  • Apply best practices for selecting assessment data
  • Understanding the purpose and types of synthetic data for evaluation
  • Understand the scope of built-in metrics
  • Select appropriate metrics based on AI system use case
  • Understand how to interpret assessment results

Module 2: Run assessments and create synthetic datasets

In this self-paced, code-first module, you use the Azure AI Evaluation SDK to run evaluations and generate synthetic datasets. This module provides a series of exercises within a Jupyter notebook created to provide step-by-step instructions for a variety of scenarios. The exercises in this module include:

  • Evaluate your model’s response using performance and quality metrics
  • Evaluate the model’s response using risk and safety indicators
  • Run assessments and track results in Azure AI Studio
  • Create a custom rater using Prompty
  • Send a query to an endpoint and run an evaluator on the resulting query and response.
  • Create synthetic datasets using conversation starters

To maximize your understanding and application of the skills you will learn, we recommend completing both modules together within your learning path.

Visit our Learning Paths to get started. aka.ms/RAI-evaluation-path!

the way forward

As generative AI continues to advance and become integrated into more aspects of our digital lives, strong evaluation practices will become increasingly important. Understanding these technologies will not only improve your current projects, but will also prepare you for better development. Trustworthy AI app.

We recommend making evaluation an integral part of your generative AI development process. Your users, stakeholders, and future selves will thank you for it.

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