Zero-shot and few-shot prompting are techniques used to elicit reasoning from large language models (LLMs). Zero-shot prompting relies on the LLM's own capabilities, without any examples or demonstrations, while few-shot prompting provides context through one or more prompts. These approaches have been successfully used in various scenarios, with zero-shot prompting providing maximum convenience but being the most challenging scenario. Few-shot prompting is superior to zero-shot prompting but has limitations, including requiring task-specific data for demonstrations and falling short of fine-tuned models. Both techniques can be utilized effectively using platforms like VectorShift, which offers no-code or SDK interfaces for prompt engineering.