prompt-learning

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

This repo includes Claude prompt curation to use Claude better.

An Open-Source Framework for Prompt-Learning.

Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%

Must-read papers on prompt-based tuning for pre-trained language models.

永久免费开源的 AIGC 课程, 目前已支持Claude Code，Codex，Hermes，OpenClaw，Obsidian，Prompt Engineering, ChatGPT, Midjourney, Runway, Stable Diffusion, AI数字人，AI声音&音乐，开源大模型

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

《面向开发者的 ChatGPT 提示词工程》非官方版中英双语字幕 Unofficial subtitles of "ChatGPT Prompt Engineering for Developers"

A curated list of awesome resources, tools, and other shiny things for LLM prompt engineering.

Create. Use. Share. ChatGPT prompts

List of Molecular and Material design using Generative AI and Deep Learning

This repository contains a collection of papers and resources on Reasoning in Large Language Models.

Welcome to the ChatGPT Prompts Library! This repository contains a diverse collection of over 100,000 prompts tailored for ChatGPT. Our prompts cover a wide range of topics, including marketing, business, fun, and much more.

A very fast, very minimal prompt optimizer

builder prompts which can be sent to builders like cursor, bolt.diy, bolt.new, trae, project idx, Claude code, lovable.dev, llmacoder, geminicoder, windsurf, deepseek-artifact, cline, aider etc

Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)

A guide to using AutoGPT for code generation and prompt engineering.

[NeurIPS 2023 Main Track] This is the repository for the paper titled "Don’t Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner"

[IJCAI 2024] Continual Multimodal Knowledge Graph Construction

we propose to generate a series of geometric shapes with target colors to disentangle (or peel off ) the target colors from the shapes. By jointly learning on multiple color-shape images, we found that the method can successfully disentangle the color and shape concepts.