I’m a big fan of what I call “bad guy” unit tests for software security. These help software developers quickly identify certain classes of software security vulnerabilities. A couple of simple examples: what happens if we stuff unexpected data into a search query? Or provide a JSON array where a string is expected?

The topic of unit tests for Large Language Models (LLM) came up this past week:

Unit tests for prompt engineering. Like it or not, reliable prompt engineering is going to be a critical part of tech stacks going forward. " Unit test LLMs with LLMs Tracking if your prompt or fine-tuned model is improving can be hard. During a hackathon, @florian_jue, @fekstroem, and I built “Have you been a good bot?”. It allows you to ask another LLM to judge the output of your model based on requirements."

Two quick thoughts:

• we’re back again with one AI assessing another AI. It’s not hard to see a slew of AI governance, safety and trust products emerging.
• AIs are great for generating unit tests and can easily be prompted to generate “bad guy” ones. If you work in security, it’s time to roll your sleeves up!