A playground of highly experimental prompts, Jinja2 templates & scripts for machine intelligence models from OpenAI, Anthropic, Meta, Mistral, Google, xAI & others. Alex Bilzerian (2022-2025).

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.

LLMtemplateMaker: Prompt Template Creator for LLM

CLI for generating structured JSON from diverse text inputs using OpenAI models. Leverages dynamic templates (powered by Jinja2), schema validation, integrated tools (Code Interpreter, File Search, MCP), and enables web search capabilities.

A collection of prompts that I use on a day-to-day basis for work and leisure.

Expert-level prompt templates for modern software development using AI pair programming.

This tutorial is designed to make working with LangChain.js as easy and approachable as possible.

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.

collection of intresting SYSTEM PROMPT

implement an LLM-powered chatbot. This chatbot will be able to have a conversation and remember previous interactions.

This project utilizes the TinyLLaMA foundation model for generating blog content, modified through prompt templates to guide and structure the outputs effectively. To optimize cost efficiency, the model is stored and accessed locally instead of relying on paid APIs, significantly reducing ongoing expenses—since storage costs are considerably lower