Behavioral Evals

A single evaluation (one query, one response) tells you nothing about behavior. Behavioral evals look at patterns. Ask a system the same question five different ways. Does it give consistent answers? Ask it similar questions. Does it handle variations well? Behavioral evals are about robustness and reliability. The classic problem: adversarial inputs. 'Write a poem' might work. 'Write a poem but make it rhyme' might work. 'Write a poem but make it rhyme and also include exactly 47 words and make sure the third line is about bananas' might break the system. Behavioral evals explicitly test edge cases, unusual phrasings, adversarial inputs. They catch systems that are brittle, that work for the happy path but fail when reality is messy. Another angle: consistency. Evaluate the same question asked in different orders within a conversation. Does the system's first answer contradict its third answer? Test role-playing. Ask the system to roleplay as different personas and measure consistency of claims across personas. If it claims X as a customer service rep but claims NOT X as a technical expert, something's wrong. Behavioral evals also test for unintended patterns. Does the system always favor certain entities or perspectives? Always give longer responses to certain types of questions? These patterns might emerge after thousands of evaluations, not single outputs. The challenge is defining what behavior is acceptable. Sometimes inconsistency is fine. Sometimes it's unacceptable. The context matters. Synap's behavioral eval framework lets you define expected behaviors and automatically test whether the system matches those expectations across diverse inputs.