#大语言模型#有关Prompt Engineering （提示工程-人工智能）指南、论文、讲座等资源
#自然语言处理#FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
#Awesome#ChatGPT 中文指南🔥，ChatGPT 中文调教指南，指令指南，应用开发指南，精选资源清单，更好的使用 chatGPT 让你的生产力 up up up! 🚀
Structured Text Generation
#大语言模型#Prompt flow 是一套开发工具，旨在简化基于 LLM 的人工智能应用程序的端到端开发周期，从构思、原型设计、测试、评估到生产部署和监控。 它使即时工程变得更加容易，并使您能够构建具有生产质量的 LLM 应用程序。
#大语言模型#🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
A collection of GPT system prompts and various prompt injection/leaking knowledge.
#大语言模型#LangGPT: Empowering everyone to become a prompt expert!🚀 Structured Prompt，Language of GPT, 结构化提示词，结构化Prompt
A blazing fast AI Gateway with integrated guardrails. Route to 200+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.
SuperPrompt is an attempt to engineer prompts that might help us understand AI agents.
#Awesome#Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.
#大语言模型#Test your prompts, agents, and RAGs. Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command ...
notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under ...
#计算机科学#Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
翻译 - 目标-记录，搜索和比较AI实验的超级简便方法
#大语言模型#Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Free prompt engineering online course. ChatGPT and Midjourney tutorials are now included!
#大语言模型#A CLI tool to convert your codebase into a single LLM prompt with source tree, prompt templating, and token counting.
#数据仓库#AI Observability & Evaluation