[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Prompt Engineering, Generative AI, and LLM Guide by Learn Prompting | Join our discord for the largest Prompt Engineering learning community

Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research

Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models"

[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models

The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework Join our Community: https://discord.com/servers/agora-999382051935506503

Streamlines and simplifies prompt design for both developers and non-technical users with a low code approach.

[ECCV 2024 Oral] DriveLM: Driving with Graph Visual Question Answering

Advanced Code and Text Manipulation Prompts for Various LLMs. Suitable for Siri, GPT-4o, Claude, Llama3, Gemini, and other high-performance open-source LLMs.

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.

A collection of ChatGPT and GPT-3.5 instruction-based prompts for generating and classifying text.

[ACL 2024] An Easy-to-use Instruction Processing Framework for LLMs.

Repo for paper "Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration"

parrot.nvim 🦜 - the plugin that brings stochastic parrots to Neovim. This is a gp.nvim-fork focused on simplicity.

Official implementation of paper "Cumulative Reasoning With Large Language Models" (https://arxiv.org/abs/2308.04371)

Data and Code for Program of Thoughts (TMLR 2023)

An extension to AUTOMATIC1111 WebUI for stable diffusion which adds a prompt generator

A codebase for "Language Models can Solve Computer Tasks"

Must-read Papers on Large Language Model (LLM) as Optimizers and Automatic Optimization for Prompting LLMs.