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
Large Language Models
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Showing 1-13 of 13 articles
Auto-CoT
Auto-CoT (Automatic Chain of Thought) is an automated prompting method that builds few-shot Chain-of-Thought demonstrations for large language models without any human-written exemplars, introduced by…
Chain of Verification (CoVe)
Chain of Verification (CoVe) is a prompting technique that reduces factual hallucinations in large language models by having the model fact-check its own draft response through a structured four-step…
Context engineering
Context engineering is the practice of designing, building, and optimizing the full set of information that a large language model receives in its context window at inference time
Graph of Thoughts
Graph of Thoughts (GoT) is a prompting and reasoning framework that models the intermediate steps of a large language model as an arbitrary directed graph rather than a linear chain or a tree.
How to Pressure LLMs for Better Output
Pressuring large language models (LLMs) is a family of prompt engineering techniques that try to push a model toward better output by adding emotional weight, urgency, stakes, or coercion to the prompt.
Least-to-Most Prompting
Least-to-Most Prompting is a few-shot prompting technique for large language models introduced by researchers at Google Brain in May 2022.
Prompt
A prompt is the input given to a generative AI model, particularly a large language model (LLM), that elicits a desired response.
Self-Discover prompting
Self-Discover is a prompting framework for large language models in which the model first composes a task-specific reasoning structure from a library of atomic reasoning modules
Self-Refine
Self-Refine is an inference-time prompting framework in which a single large language model iteratively improves its own output by alternating between generating natural-language feedback on a draft and…
Self-consistency
Self-consistency is a decoding strategy for large language models that samples multiple chain-of-thought reasoning paths for the same question and returns the answer that the majority of those paths agree on
Step-Back Prompting
Step-Back Prompting is a two-stage prompting technique introduced by researchers at Google DeepMind in October 2023.
System prompt
A system prompt is a special set of instructions, guidelines, persona definitions, and contextual information given to a large language model (LLM) before any user input