An LLM emits intermediate reasoning steps as part of its responses.

Comprehensive and Detailed In-Depth Explanation:

Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn’t focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.

OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.