Evals (Evaluation Systems)

You ship code to production, you run tests. You should ship AI systems to production, you run evals. Evals are systematic evaluations of how well your system works. But unlike traditional software tests where you check 'does this function return true for this input,' evals measure subjective qualities. Does this response seem accurate? Helpful? Safe? The challenge is scale and automation. You can't manually review every response. So evals use automated metrics, reference answers, or learned models to score outputs. Types vary. Accuracy evals compare outputs against gold-standard references (BLEU score, exact match, semantic similarity). Safety evals check if outputs contain harmful content. Efficiency evals measure latency and cost. Behavioral evals check if the system behaves consistently. Most robust systems use multiple evals because different evals catch different problems. A system might score well on accuracy evals but fail safety evals. Or vice versa. The gold-standard reference problem is real though. Sometimes there's no single correct answer. An AI can generate three totally different but equally good code implementations. Your eval framework needs to handle that, either by accepting multiple reference answers or using learned evaluators. LLM-as-judge is increasingly common: another LLM scores outputs on some rubric. Works surprisingly well but introduces circularity (bias in one model compounds). The iteration loop matters. Run evals early and often. Use them to catch regressions (new code made something worse). Use them to compare approaches (should we try fine-tuning or RAG?). Use them to identify failure modes (what kinds of queries does our system consistently miss?). Synap's eval framework integrates multiple evaluation strategies, letting developers measure quality across dimensions that matter for their specific application and catch problems before production.