LLMs, GenAI, RAG, agents, evaluation · insight-0536

Measure chain-of-thought prompting per task instead of switching it on everywhere

Situation
After reading that step-by-step prompting improves reasoning, a team adds it to every prompt, including simple classification.
Action
Turn chain-of-thought on per task based on an A/B comparison on the eval set, measure accuracy, latency and tokens, and strip or hide intermediate reasoning from customer-facing outputs.
Reason
Chain-of-thought gains were reported for multi-step reasoning tasks in large models; tasks without multi-step structure may gain little while paying the extra tokens.
Boundary
Prompt design for any task mix.

Symptom or risk

Latency and token use rise across the board while only a few tasks benefit, and reasoning text leaks into outputs meant for customers.

Models with built-in reasoning modes need different controls; check their documentation.

Per-task settings add configuration.

Treating the reasoning text as a faithful explanation of the answer.

Wei et al. found chain-of-thought exemplars improved performance on arithmetic, commonsense and symbolic reasoning tasks, with gains emerging in sufficiently large models.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models