Source URL: https://simonwillison.net/2025/Jul/3/faqs-about-ai-evals/#atom-everything
Source: Simon Willison’s Weblog
Title: Frequently Asked Questions (And Answers) About AI Evals
Feedly Summary: Frequently Asked Questions (And Answers) About AI Evals
Hamel Husain and Shreya Shankar have been running a paid, cohort-based course on AI Evals For Engineers & PMs over the past few months. Here Hamel collects answers to the most common questions asked during the course.
There’s a ton of actionable advice in here. I continue to believe that a robust approach to evals is the single most important distinguishing factor between well-engineered, reliable AI systems and YOLO cross-fingers and hope it works development.
Hamel says:
It’s important to recognize that evaluation is part of the development process rather than a distinct line item, similar to how debugging is part of software development. […]
In the projects we’ve worked on, we’ve spent 60-80% of our development time on error analysis and evaluation. Expect most of your effort to go toward understanding failures (i.e. looking at data) rather than building automated checks.
I found this tip to be useful and surprising:
If you’re passing 100% of your evals, you’re likely not challenging your system enough. A 70% pass rate might indicate a more meaningful evaluation that’s actually stress-testing your application.
Via Hacker News
Tags: ai, generative-ai, llms, hamel-husain, evals
AI Summary and Description: Yes
Summary: The text discusses the significance of evaluation processes in developing AI systems, emphasizing that rigorous evaluation is crucial for the reliability of AI applications. The content is highly relevant for professionals in AI security as it underscores the importance of error analysis in ensuring robust system performance.
Detailed Description: The text presents insights from Hamel Husain and Shreya Shankar, who have been conducting a course focused on AI evaluations. Key points made by Hamel include:
– **Evaluation as a Development Process**: Emphasizes that evaluation should be integrated into the development cycle, akin to debugging in software development. This perspective reinforces the need to treat evaluations as ongoing rather than as a checkbox activity.
– **Time Investment in Error Analysis**: Highlights that a significant portion (60-80%) of development time should be allocated to error analysis and evaluation. This statistic suggests that understanding potential failures is critical to enhancing the reliability and trustworthiness of AI systems.
– **Challenging Evaluations**: A surprising recommendation suggests that a system passing 100% of its evaluations could indicate insufficient challenge. A more realistic passing rate of around 70% may offer a better gauge of system robustness and could suggest that the application is being rigorously tested for edge cases and failure points.
Overall, this discussion is particularly relevant for security and compliance professionals in AI and communicates a vital practice: robust evaluation processes are essential for creating reliable systems, supporting the broader agenda of AI safety and efficacy.
– *Implications for AI Security*:
– Encourages a proactive approach to identifying and mitigating vulnerabilities.
– Reinforces the integration of continuous evaluation into security protocols for AI systems.
– Emphasizes the importance of creating a culture around challenge and iterative testing within AI development teams.
Professionals engaged in AI and related sectors should ensure that their evaluation processes are comprehensive and challenging enough to foster resilience against potential failures or security threats.