Hypothesis vs. Hallucinations: Property Testing AI-Generated Code

Large Language Models can generate code in a flash, but that code is notoriously unreliable. Traditional unit tests often can’t put enough guardrails in place to ensure correctness… even if they’re written by the LLM itself.
This is where property-based testing (PBT) becomes essential.
Today, we're joined by David R. MacIver, creator of the PBT library Hypothesis, and now an Antithesis employee! We discuss how to build robust feedback loops that are needed to make AI-generated code trustworthy.
We'll cover why standard AI coding benchmarks are flawed, how Hypothesis makes PBT approachable…