People are writing great tools and papers for improving outputs from GPT. Here are some cool ones we've seen:
Prompting libraries & tools (in alphabetical order)
- Arthur Shield: A paid product for detecting toxicity, hallucination, prompt injection, etc.
- Chainlit: A Python library for making chatbot interfaces.
- FLAML (A Fast Library for Automated Machine Learning & Tuning): A Python library for automating selection of models, hyperparameters, and other tunable choices.
- Guardrails.ai: A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs.
- Guidance: A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
- Haystack: Open-source LLM orchestration framework to build customizable, production-ready LLM applications in Python.
- LangChain: A popular Python/JavaScript library for chaining sequences of language model prompts.
- LiteLLM: A minimal Python library for calling LLM APIs with a consistent format.
- LlamaIndex: 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: An open-source library for evaluating task performance of language models and prompts.
- Outlines: 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: A small Python library for using language models to perform NLP tasks.
- PromptPerfect: A paid product for testing and improving prompts.
- Prompttools: 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: 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: An open-source GenAI-Ops tool for tuning and evaluating prompts, retrieval configurations, and model parameters using customizable datasets, evaluation methods, and evolution strategies.
Prompting guides
- Brex's Prompt Engineering Guide: 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.
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.
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
code-davinci-002'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
GPT-4'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
text-davinci-002'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 Reasoning steps (thinking about what to do) and Acting (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%.