awesome-prompts
Chatbot prompts
A curated list of prompts for improving AI chatbot performance
Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.
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Open GPTs Prompts | |||
| 💻Professional Coder | |||
| prompt | 5,308 | 2 months ago | |
| 👌Academic Assistant Pro | |||
| prompt | 5,308 | 2 months ago | |
| ✏️All-around Writer | |||
| prompt | 5,308 | 2 months ago | |
| 📗All-around Teacher | |||
| prompt | 5,308 | 2 months ago | |
| AutoGPT | |||
| prompt | 5,308 | 2 months ago | (The prompt is urgly and not stable now, let's improve it together!) |
Other GPTs | |||
| Auto Literature Review Link | |||
| Scholar GPT Pro Link | |||
| Paraphraser & Proofreader Link | |||
| AI Detector Pro Link | |||
| Paper Review Pro Link | |||
| Auto Thesis PPT Link | |||
| Paper Interpreter Pro Link | |||
| Data Analysis Link | |||
| PDF Translator Link | |||
| AI Detector Link | |||
| AutoGPT Link | |||
| TeamGPT Link | |||
| GPT Link | |||
| AwesomeGPTs Link | |||
| Prompt Engineer Link | |||
| Paimon Link | |||
| Link | |||
| Jessica Link | |||
| Logo Designer Link | |||
| Text Adventure RGP Link | |||
| Alina Link | |||
| My Boss Link | |||
| My Excellent Classmates Link | |||
| I Ching divination Link | |||
Excellent Prompts From Community | |||
| prompt | 5,308 | 2 months ago | |
| github link | 28,935 | 8 months ago | |
| prompt | 5,308 | 2 months ago | |
| discord | |||
| prompt | 5,308 | 2 months ago | |
| discord | |||
| prompt | 5,308 | 2 months ago | |
| discord | |||
| prompt | 5,308 | 2 months ago | |
| discord | |||
| prompt | 5,308 | 2 months ago | |
| discord | |||
| prompt | 5,308 | 2 months ago | |
| discord | |||
| prompt | 5,308 | 2 months ago | |
| paper | |||
| prompt | 5,308 | 2 months ago | |
| discord | |||
Advanced Prompt Engineering | |||
| https://ar5iv.labs.arxiv.org/html/2307.15337 | |||
| https://ar5iv.labs.arxiv.org/html/2308.09687 | |||
| https://ar5iv.labs.arxiv.org/html/2305.16582 | |||
| https://ar5iv.labs.arxiv.org/html/2308.10379 | |||
| https://ar5iv.labs.arxiv.org/html/2104.01431 | |||
| https://ar5iv.labs.arxiv.org/html/2302.12822 | |||
| https://ar5iv.labs.arxiv.org/html/2210.03493 | |||
| https://ar5iv.labs.arxiv.org/html/2305.15408 | |||
| https://ar5iv.labs.arxiv.org/html/2212.10509 | |||
| https://ar5iv.labs.arxiv.org/html/2305.17812 | |||
| https://ar5iv.labs.arxiv.org/html/2301.13379 | |||
| https://ar5iv.labs.arxiv.org/html/2212.10001 | |||
| https://ar5iv.labs.arxiv.org/html/2305.04091 | |||
| https://ar5iv.labs.arxiv.org/html/2310.06692 | |||
| https://ar5iv.labs.arxiv.org/html/2205.11916 | |||
Related resources about Prompt Engineering / Prompting libraries & tools (in alphabetical order) | |||
| Chainlit | : A Python library for making chatbot interfaces | ||
| Embedchain | 22,943 | 2 days ago | : A Python library for managing and syncing unstructured data with LLMs |
| FLAML (A Fast Library for Automated Machine Learning & Tuning) | : A Python library for automating selection of models, hyperparameters, and other tunable choices | ||
| GenAIScript | : JavaScript-ish scripts to create execute prompts, extract structured data, integrated in Visual Studio Code | ||
| Guardrails.ai | : A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs | ||
| Guidance | 19,137 | 3 days ago | : A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control |
| Haystack | 17,817 | 2 days ago | : Open-source LLM orchestration framework to build customizable, production-ready LLM applications in Python |
| HoneyHive | : An enterprise platform to evaluate, debug, and monitor LLM apps | ||
| LangChain | 95,185 | 2 days ago | : A popular Python/JavaScript library for chaining sequences of language model prompts |
| LiteLLM | 14,080 | 2 days ago | : A minimal Python library for calling LLM APIs with a consistent format |
| LlamaIndex | 36,899 | 2 days ago | : A Python library for augmenting LLM apps with data |
| LMQL | : A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools | ||
| OpenAI Evals | 15,069 | about 2 months ago | : An open-source library for evaluating task performance of language models and prompts |
| Outlines | 9,567 | 2 days ago | : A Python library that provides a domain-specific language to simplify prompting and constrain generation |
| Parea AI | : A platform for debugging, testing, and monitoring LLM apps | ||
| Portkey | : A platform for observability, model management, evals, and security for LLM apps | ||
| Promptify | 3,276 | 8 months ago | : A small Python library for using language models to perform NLP tasks |
| PromptPerfect | : A paid product for testing and improving prompts | ||
| Prompttools | 2,718 | 3 months ago | : Open-source Python tools for testing and evaluating models, vector DBs, and prompts |
| Scale Spellbook | : A paid product for building, comparing, and shipping language model apps | ||
| Semantic Kernel | 22,046 | 2 days ago | : A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning |
| Weights & Biases | : A paid product for tracking model training and prompt engineering experiments | ||
| YiVal | 2,658 | 7 months ago | : An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies |
Related resources about Prompt Engineering / Prompting guides | |||
| Brex's Prompt Engineering Guide | 8,448 | about 1 year ago | : Brex's introduction to language models and prompt engineering |
| learnprompting.org | : An introductory course to prompt engineering | ||
| Lil'Log Prompt Engineering | : An OpenAI researcher's review of the prompt engineering literature (as of March 2023) | ||
| OpenAI Cookbook: Techniques to improve reliability | : A slightly dated (Sep 2022) review of techniques for prompting language models | ||
| promptingguide.ai | : A prompt engineering guide that demonstrates many techniques | ||
| Xavi Amatriain's Prompt Engineering 101 Introduction to Prompt Engineering | and : A basic but opinionated introduction to prompt engineering and a follow up collection with many advanced methods starting with CoT | ||
Related resources about Prompt Engineering / Video courses | |||
| Andrew Ng's DeepLearning.AI | : A short course on prompt engineering for developers | ||
| Andrej Karpathy's Let's build GPT | : A detailed dive into the machine learning underlying GPT | ||
| Prompt Engineering by DAIR.AI | : A one-hour video on various prompt engineering techniques | ||
| Scrimba course about Assistants API | : A 30-minute interactive course about the Assistants API | ||
| LinkedIn course: Introduction to Prompt Engineering: How to talk to the AIs | : Short video introduction to prompt engineering | ||
Related resources about Prompt Engineering / Papers on advanced prompting to improve reasoning | |||
| Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022) | : Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) rises from 18% to 57% | ||
| Self-Consistency Improves Chain of Thought Reasoning in Language Models (2022) | : Taking votes from multiple outputs improves accuracy even more. Voting across 40 outputs raises PaLM's score on math word problems further, from 57% to 74%, and 's from 60% to 78% | ||
| Tree of Thoughts: Deliberate Problem Solving with Large Language Models (2023) | : Searching over trees of step by step reasoning helps even more than voting over chains of thought. It lifts 's scores on creative writing and crosswords | ||
| Language Models are Zero-Shot Reasoners (2022) | : Telling instruction-following models to think step by step improves their reasoning. It lifts 's score on math word problems (GSM8K) from 13% to 41% | ||
| Large Language Models Are Human-Level Prompt Engineers (2023) | : Automated searching over possible prompts found a prompt that lifts scores on math word problems (GSM8K) to 43%, 2 percentage points above the human-written prompt in Language Models are Zero-Shot Reasoners | ||
| Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling (2023) | : Automated searching over possible chain-of-thought prompts improved ChatGPT's scores on a few benchmarks by 0–20 percentage points | ||
| Faithful Reasoning Using Large Language Models (2022) | : Reasoning can be improved by a system that combines: chains of thought generated by alternative selection and inference prompts, a halter model that chooses when to halt selection-inference loops, a value function to search over multiple reasoning paths, and sentence labels that help avoid hallucination | ||
| STaR: Bootstrapping Reasoning With Reasoning (2022) | : Chain of thought reasoning can be baked into models via fine-tuning. For tasks with an answer key, example chains of thoughts can be generated by language models | ||
| ReAct: Synergizing Reasoning and Acting in Language Models (2023) | : For tasks with tools or an environment, chain of thought works better if you prescriptively alternate between asoning steps (thinking about what to do) and ing (getting information from a tool or environment) | ||
| Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) | : Retrying tasks with memory of prior failures improves subsequent performance | ||
| Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023) | : Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches | ||
| Improving Factuality and Reasoning in Language Models through Multiagent Debate (2023) | : Generating debates between a few ChatGPT agents over a few rounds improves scores on various benchmarks. Math word problem scores rise from 77% to 85% | ||