> An agent doing a task with 1 example is one shot. An agent doing a task with a few examples is few shot. I don't think you are correctly using these terms Th

> An agent doing a task with 1 example is one shot. An agent doing a task with a few examples is few shot. I don't think you are correctly using these terms This is a different thing. Yes, giving multiple example is called "few-shot prompting". But one-shot vs few-shot benchmarking is different. In this context "one-shot" means "pass at 1 effort" as opposed to "multi-shot". In the literature this is called "pass@k". Anthropic has a good explanation here: https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents https://www.anthropic.com/engineering/demystifying-evals-for... (search for "pass@k"). In this discussion we are discussing pass@1 (single shot) vs pass@(k>1) (multi shot). > The multiple back to back LLM calls are done on accumulating context, so if there is a sampling error it could throw the entire session out of whack, because LLM's build on the previous context. This isn't really true. In an agentic loop the LLM can correct itself via in-context learning.