Few Shot

Prompt Engineering: Lilian Weng's Comprehensive Survey

Lilian Weng's canonical reference on prompt engineering techniques — zero-shot, few-shot, chain-of-thought, self-consistency, tree-of-thoughts, and more — grounded in research papers. Still the most comprehensive single-author survey of the space.

STaR: Bootstrapping Reasoning With Reasoning

Pretrain, Prompt, Predict: NLP Survey

A comprehensive survey of the Pretrain, Prompt, Predict paradigm in NLP — covering prompt engineering, answer engineering, and in-context learning as a unified framework. The academic backbone behind what practitioners call 'prompt engineering'.

Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

Reynolds and McDonell (2021) argue that prompting large language models is better understood as programming than as few-shot learning — the few examples in a prompt aren't training data but rather code that specifies the desired computation. This reframing opens up principled prompt design strategies that beat naive templating.