My personal prompt library for various LLMs + scripts & tools. Suitable for models from Deepseek, OpenAI, Claude, Meta, Mistral, Google, Grok, and others.
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Updated
Mar 18, 2025 - Python
My personal prompt library for various LLMs + scripts & tools. Suitable for models from Deepseek, OpenAI, Claude, Meta, Mistral, Google, Grok, and others.
All in One AI Chat Tool( GPT-4 / GPT-3.5 /OpenAI API/Azure OpenAI/Prompt Template Engine)
Awesome ChatGPT Prompts: A list of Awesome ChatGPT prompts to make ChatGPT better. The Awesome ChatGPT Prompts Github repository contains over 157+ Prompts that will increase your productivity. For more ChatGPT Prompts visit: https://www.chatgpt-prompts.net/
Collection of Basic Prompt Templates for Various Chat LLMs (Chat LLM 的基础提示模板集合)
Interactive notes (Jupyter Notebooks) for building AI-powered applications
AI-zhipu is an Obsidian plugin that helps you utilize the Zhipu API. 智谱AI obsidian 插件
vim as a perfect large language models prompts playground
Prompt templating and versioning using jinja2 and litellm 🔥
Flexible and lightweight library for creating prompt templates
One Prompt that can Turned ChatGPT into AutoGPT.
Expert-level prompt templates for modern software development using AI pair programming.
LLMtemplateMaker: Prompt Template Creator for LLM
This tutorial is designed to make working with LangChain.js as easy and approachable as possible.
ostruct uses OpenAI Structured Output APIs to process a set of plain text files (data, reports, source code, CSV, etc), input variables, a dynamic prompt template, and a JSON schema specifying the desired output format, and will produce the result in JSON format.
This repository, forked from Packt Publishing, serves as a comprehensive guide to LangChain and LLMs, encompassing all the resources and knowledge gained from the on-demand course.
implement an LLM-powered chatbot. This chatbot will be able to have a conversation and remember previous interactions.
A collection of prompts that I use on a day-to-day basis for work and leisure.
QA on documents with LangChain framework and Hugging Face LLM and prompt Templates
"Will you be able to win?" This is the game that challenges Human to face LLM models in a variety of games, from guessing to association/pattern games... challenge yourself!
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