Chain-of-Thought (CoT) prompting is a technique specifically designed to elicit reasoning from a Large Language Model (LLM). It involves instructing the model to break down a complex problem into a series of intermediate, sequential steps before providing a final answer. By explicitly prompting the model to "think step-by-step," it externalizes its reasoning process, which often improves accuracy on tasks requiring arithmetic, commonsense, or symbolic reasoning. This method makes the model's path to a solution transparent and verifiable.

A. Step-Back Prompting: This technique involves abstracting away from specific details to derive general principles first, then applying those principles to solve the problem, which is different from a direct step-by-step breakdown.

C. Least-to-Most Prompting: This is a more specific strategy that decomposes a problem into a sequence of simpler subproblems, where the solution to one subproblem is used to solve the next.

D. In-Context Learning: This is a general capability of LLMs to learn from examples provided within the prompt (e.g., few-shot prompting), not the specific technique of generating reasoning steps.

1. Oracle Cloud Infrastructure (OCI) Documentation: In the "Best Practices for Prompts" for the Generative AI service, it states, "Chain-of-thought (CoT) prompting enables complex reasoning capabilities through a series of intermediate reasoning steps. You can combine it with few-shot prompting to get better results on more complex tasks that require reasoning before the response."

Source: Oracle Cloud Infrastructure Documentation, Generative AI, "Best Practices for Prompts," Section: "Chain-of-Thought Prompting."

2. Peer-Reviewed Academic Publication: The foundational paper on the topic defines the technique as a method to encourage the model to generate a series of intermediate steps. The abstract states, "...we explore chain-of-thought prompting as a simple and broadly applicable method for enhancing reasoning in language models... chain-of-thought prompting allows models to decompose multi-step problems into intermediate steps..."

Source: Wei, J., Wang, X., Schuurmans, D., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." Advances in Neural Information Processing Systems, 35, 24824-24837. (Available via NeurIPS Proceedings).

3. University Courseware: Stanford University's course on LLMs describes CoT as a method to improve performance on complex reasoning tasks by prompting the model to generate intermediate reasoning steps.

Source: Stanford University, CS324: Large Language Models, Winter 2024, Lecture 10: "Prompting, Instruction Tuning, and RLHF," Section on Chain-of-Thought.