A context engineer is reviewing MLflow experiment tracking results comparing two system prompt configurations for a Databricks agent built to assist with contract review. Configuration P uses a role declaration, a full task instruction, and a dedicated edge-case handling section that describes in prose how the agent should respond to ambiguous or overlapping clause types, averaging 205 tokens per call and an 81% clause-classification accuracy. Configuration Q retains the role declaration, removes the prose edge-case handling section, shortens the task instruction to a concise directive, and adds three few-shot examples each demonstrating how the agent should classify a distinct edge-case clause type, averaging 340 tokens per call and a 93% clause-classification accuracy. The token budget increases by 135 tokens per call between Configuration P and Configuration Q. Latency increases from 290 ms to 380 ms. Which conclusion is supported by these experiment tracking results?
