Agentic Context Engineering
Agentic Context Engineering (ACE) is a framework for scalable and efficient context adaptation in large language models (LLMs) that lets an AI system improve itself by treating its own context as an evolving
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Chain-of-Thought
Chain-of-thought (CoT) is a sequence of intermediate steps that a language model generates before its final answer.
Meta Prompting
Meta prompting (also spelled meta-prompting) is an advanced prompt engineering technique where large language models (LLMs) are used to generate, refine, critique, select, or optimize prompts for themselves or…
Zero shot, one shot and few shot learning
Zero-shot, one-shot, and few-shot learning are three related settings in machine learning and prompt engineering defined by how many labelled examples a model sees of a target task or class before making a…