AI evals

AI evaluations — or evals — involve systematically assessing an LLM's accuracy and reliability against predefined metrics and business objectives. This ensures AI models deliver on their intended purpose while also minimizing risks and ensuring alignment with an organization’s wider objectives.

Being able to demonstrate an LLM is effective and reliable is valuable because it makes it possible to move from an experimental proof of concept to something that can be deployed in a real-world environment with greater confidence.

What’s in it for you?

AI evals:

• Build trust and confidence. Generative AI is inherently unpredictable; being able to test and determine a system is reliable and accurate (in terms of its outputs) is essential for organizations that want to actually deploy AI in a “live” environment.

• Ensure accuracy and reliability. Evals verify the AI performs its tasks correctly and consistently. This is crucial for applications where errors could have significant consequences, such as in financial forecasting, medical diagnosis or autonomous systems.

• Mitigate common AI risks, such as bias. Rigorous evaluations help identify and address potential biases within the AI, ensuring fair and equitable outcomes. They also catch errors or vulnerabilities before deployment, reducing the risk of costly mistakes, legal issues or reputational damage.

• Drive improvement. Evals provide actionable insights that inform future development. They highlight areas where AI can be refined, leading to better performance and enhanced capabilities over time.

What are the trade-offs of AI evals?

• Cost and time. Thorough AI evaluations can be expensive and time-consuming, requiring specialized expertise, data and computational resources. This can be a significant burden, especially for smaller organizations.

• Data dependency. The quality of evals heavily relies on the quality of the data that’s used for testing. Biased or insufficient data can lead to misleading eval results.

• Defining success. What constitutes "good" performance can be subjective and vary based on the AI's purpose and context. Defining clear, measurable evaluation metrics can be challenging.

• Trade-offs within AI. Often, improving one aspect of an AI (such as accuracy) might degrade another (like explainability or fairness). Businesses need to decide which trade-offs are acceptable based on their priorities and ethical considerations. For instance, a highly accurate AI for medical diagnosis might be less explainable, which would raise significant concerns about trust and accountability.

• The dynamic nature of AI. AI models continuously learn and adapt, which means an evaluation done at one point in time might not hold true later. This means continuous monitoring and re-evaluation are essential.

How are AI evals being used?

There are two key types of AI evals, used at different points in the development process: pre-deployment validation and production evaluations.

• Pre-deployment validation is done during the development process. Typically, this involves performance measurement and benchmarking, helping developers understand the impact of their design and architecture choices. It’s also important for ensuring accuracy and performance is maintained — sometimes, due to the dynamic nature of AI systems, changes to the code, data or model parameters can negatively impact the AI.

• Post-deployment and production evaluation involves effective monitoring and oversight to detect issues with outputs and performance.

   

Ultimately, AI evaluations give businesses the confidence that AI can be safely and effectively deployed into the real world. They also provide the necessary oversight and control on what are dynamic and changing systems, helping businesses manage their risks proactively and, if needed, quickly.