Evaluating generative AI: Best practices for developers

As a developer working with generative AI, you've likely marveled at the impressive outputs your models can produce. But how do you ensure these outputs consistently meet your quality standards and business requirements? Enter the essential world of generative AI evaluation!

 

Why evaluation matters

 

Evaluating generative AI output is not just a best practice—it's essential for building robust, reliable applications. Here's why:

1. Quality assurance: Ensures your AI-generated content meets your standards.
2. Performance tracking: Helps you monitor and improve your app’s performance over time.
3. User trust: Builds confidence in your AI application among end-users.
4. Regulatory compliance: Helps meet emerging AI governance requirements.

 

Best practices for generative AI evaluation

 

In the rapidly advancing field of generative AI, ensuring the reliability and quality of your apps’ output is paramount. As developers, we strive to create applications that not only astound users with their capabilities but also maintain a high level of trust and integrity. Achieving this requires a systematic approach to evaluating our AI systems. Let’s dive into some best practices for evaluating generative AI.

 

Define clear metrics

Establishing clear metrics serves as the cornerstone for evaluating the efficacy and reliability of your app. Without well-defined criteria, the evaluation process can become subjective and inconsistent, leading to misleading conclusions. Clear metrics transform abstract notions of "quality" into tangible, measurable targets, providing a structured framework that guides both development and iteration. This clarity is crucial for aligning the output with business goals and user expectations.

 

Context is key

Always evaluate outputs in the context of their intended use case. For example, while generative AI used in a creative writing app may prioritize originality and narrative flow, these same criteria would be inadequate for evaluating a customer support app. Here, the primary metrics would focus on accuracy and relevance to user queries. The context in which the AI operates fundamentally shifts the framework of evaluation, demanding tailored criteria that align with the specific goals and user expectations of the application. Therefore, understanding the context ensures that the evaluation process is both relevant and rigorous, providing meaningful insights that drive improvement.

 

Use a multi-faceted approach

Relying on a single method for evaluating generative AI can yield an incomplete and potentially skewed understanding of its performance. By adopting a multi-faceted approach, you leverage the strengths of various evaluation techniques, providing a more holistic view of your AI's capabilities and limitations. This comprehensive strategy combines quantitative metrics and qualitative assessments, capturing a broader range of performance indicators.

 

Quantitative metrics, such as perplexity and BLEU scores, offer objective, repeatable measurements that are essential for tracking improvements over time. However, these metrics alone often fall short of capturing 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, accounting for context and subjective experience that automated metrics might miss.

 

Implement continuous evaluation

The effectiveness and reliability of your applications are not static metrics. They require regular and ongoing scrutiny to ensure they consistently meet the high standards set forth during their development. Continuous evaluation is therefore essential, as it allows developers to identify and rectify issues in real-time, ensuring that the AI systems adapt to new data and evolving user needs. This approach fosters a proactive stance, enabling swift improvements and maintaining the trust and satisfaction of the end-users.

 

Frequent and scheduled evaluations should be embedded into the development cycle. Ideally, evaluations should be conducted after every significant iteration or update to the AI model or system prompt. Additionally, periodic assessments, perhaps monthly or quarterly, can help in tracking the long-term performance and stability of the AI system. By maintaining this rhythm, developers can quickly respond to any degradation in quality, keeping the application robust and aligned with its intended objectives.

 

Don't treat evaluation as a one-time task! Set up systems for ongoing monitoring and feedback loops.

 

Dive deeper with our new evaluations Learn Path

 

We're excited to announce our new Learn path designed to take your evaluation skills to the next level!

 

Module 1: Evaluating generative AI applications

In this module, you’ll learn the fundamental concepts of evaluating generative AI applications. This module serves as a great starting point for anyone who’s new to evaluations in the context of generative AI. This module explore topics such as:

• Applying best practices for choosing evaluation data
• Understanding the purpose of and types of synthetic data for evaluation
• Comprehending the scope of built-in metrics
• Choosing the appropriate metrics based on your AI system use case
• Understanding how to interpret evaluation results

 

Module 2: Run evaluations and generate synthetic datasets

In this self-paced, code-first module, you'll run evaluations and generate synthetic datasets with the Azure AI Evaluation SDK. This module provides a series of exercises within Jupyter notebooks, created to provide step-by-step instruction across various scenarios. The exercises in this module include:

• Assessing a model’s response using performance and quality metrics
• Assessing a model’s response using risk and safety metrics
• Running an evaluation and tracking the results in Azure AI Studio
• Creating a custom evaluator with Prompty
• Sending queries to an endpoint and running evaluators on the resulting query and response
• Generating a synthetic dataset using conversation starters

 

We recommend completing both modules together within the Learn path to maximize comprehension by applying the skills that you’ll learn!

 

Visit the Learn path to get started: aka.ms/RAI-evaluations-path!

 

The path forward

 

As generative AI continues to evolve and integrate into more aspects of our digital lives, robust evaluation practices will become increasingly critical. By understanding these techniques, you're not just improving your current projects—you're better prepared to develop trustworthy AI apps.

 

We encourage you to make evaluation an integral part of your generative AI development process. Your users, stakeholders, and future self will thank you for it.

 

Happy evaluating!