I compared three ways to extract decisions from LLMs: plain decoding, JSON‑masked structured output, and Jev‑style scoring, and noted when each is best suited.
Plain decoding generates full text token‑by‑token; the application then parses the result. With normal LLM decoding, the model goes through a full reasoning process.
Structured‑output decoding uses a grammar or schema that masks illegal next tokens at every step, forcing the model to emit well‑formed JSON token‑by‑token. A grammar or schema masks illegal next tokens at every step.
Jev‑style scoring supplies a fixed list of candidate labels and returns probabilities, e.g., billing → 0.90, technical support → 0.10, account access → 0.00.
In a shared‑adapter deployment on an RTX 4090 via Runpod Serverless, performance hit 27.9 requests per second and 795.7 tokens per second at peak, while GPU utilization reached 93%. It reached 27.9 requests per second and 795.7 tokens per second at peak, while GPU utilization reached 93%.