Jev vs LLM classification
When to gate with System One probabilities instead of asking a chat model to label things.
Probabilities you can threshold
Jev returns explicit probability mass over options. Instead of parsing YES/NO from chat output, you set if (confidence > 0.9) in code. That matters for moderation, fraud, and safety pipelines.
Parallel questions
Ask many typed questions in one request over the same state. LLM classification often serializes prompts or pays for redundant context tokens.
When LLMs still win
Open-ended drafting, long reasoning chains, and novel text generation remain LLM territory. Many production stacks use Jev for discrete decisions and LLMs for language-heavy steps. Browser Use Ultrafast is a well-known hybrid.
Cost and token math
Generative classifiers often resend long instructions and examples on every call. System One calls keep prompts compact because questions are typed fields, not paragraphs of rubric text. For high-volume gates that difference shows up in both latency and spend.
Evaluation and testing
Fixed option sets make golden tests easier: feed recorded state, expect option B above 0.8. LLM label parsing tests brittle string contains checks. Teams migrating often keep LLM baselines while they calibrate Jev thresholds on production logs.
Directory examples
Browse moderation and routing listings in this directory for repos that publish benchmarks against chat baselines. Treat them as patterns, not endorsements. Always read the linked source for maintenance status.
Know someone still labeling with chat?
Send them this guide. Low spam, high signal.
Drop it in Slack. Tag TypeSafe if it saves someone a sprint.
More context on Jev Directory and the llms.txt map for AI crawlers.