Showing 1-27 of 27 articles
26 Principles of Good Prompts
See also: Prompt engineering for text generation and Guides The 26 principles of good prompts are a set of 26 practical rules for writing prompts to large...
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...
AI AgentsArtificial Intelligence
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...
Large Language Models
Chain of Density prompting
Chain of Density (CoD) is a prompting technique for abstractive text summarization with large language models, introduced in the 2023 paper "From Sparse to...
Natural Language Processing
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...
Large Language Models
Chain-of-Thought
Chain-of-thought (CoT) prompting is a prompt engineering technique that improves the reasoning ability of large language models by having them generate a...
Deep LearningMachine Learning
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...
AI AgentsArtificial Intelligence
CustomGPT Instructions for Knowledge (Uploaded Files)
"CustomGPT Instructions for Knowledge (Uploaded Files)" is the informal name for a short block of default instructions that OpenAI prepends to a Custom GPT's...
ChatGPTOpenAI
Fine-tune ChatGPT with Perplexity, Burstiness, Professionalism, Randomness and Sentimentality Guide
The perplexity, burstiness, professionalism, randomness, and sentimentality guide is a prompt engineering pattern that asks ChatGPT to write according to five...
ChatGPT
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...
Large Language Models
How to Pressure LLMs for Better Output
See also: Prompt engineering, Chain-of-thought prompting, LLM anxiety, and Large language model Pressuring large language models (LLMs) is a family of prompt...
Large Language ModelsNatural Language Processing
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. The method addresses...
Large Language Models
Meta Prompting
Meta prompting (also spelled meta-prompting) is an advanced prompt engineering technique where large language models (LLMs) are used to generate, refine,...
Artificial IntelligenceMachine Learning
MidJourney Prompt Generator
MidJourney Prompt Generator is a generic category name that refers to the broad class of tools, custom GPTs, and prompt templates that help users compose...
AI Tools & ProductsImage Generation
Prompt
A prompt is the input given to a generative AI model, particularly a large language model (LLM), that elicits a desired response. In the simplest case, a...
Large Language Models
Prompt engineering for image generation
Prompt engineering for image generation is the practice of writing and refining the text prompts that guide text-to-image AI image generation models such as...
Prompt engineering for text generation
Prompt engineering for text generation is the practice of designing the natural-language input (the prompt) given to a large language model so that it produces...
ReAct (prompting)
ReAct (short for Reasoning and Acting) is a prompting paradigm for large language models that interleaves verbal reasoning traces ("Thoughts") with...
AI AgentsReasoning Models
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...
Large Language Models
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...
Large Language Models
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...
Large Language ModelsReasoning Models
Skeleton-of-Thought
Skeleton-of-Thought (SoT) is a prompting technique for large language models that reduces end-to-end generation latency by first eliciting a short outline of...
AI Inference
Step-Back Prompting
Step-Back Prompting is a two-stage prompting technique introduced by researchers at Google DeepMind in October 2023. It elicits stronger reasoning from large...
Large Language Models
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...
AI SafetyLarge Language Models
Tree of Thoughts
Tree of Thoughts (ToT) is a prompting and inference-time search framework for large language models that lets the model explore multiple intermediate reasoning...
Artificial IntelligenceReasoning Models
Vibe Coding Tips and Tricks
See also: Vibe Coding Vibe coding is the practice of building software by describing what you want in natural language and letting an AI write the code. The...
AI Code GenerationSoftware Development
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...
Machine Learning