Plan-And-Solve-Prompting

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Plan-and-Solve-Prompting is an open-source prompt engineering tool that improves zero-shot chain-of-thought reasoning in large language models. It provides code and resources for implementing advanced prompting techniques.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Plan-and-Solve-Prompting?

Plan-and-Solve-Prompting offers a method for enhancing the zero-shot chain-of-thought reasoning capabilities of large language models. This open-source project, based on an ACL 2023 paper, provides the necessary code and resources for researchers and developers to implement and experiment with advanced prompting strategies. Notably, the Plan-and-Solve Prompting technique has been integrated into the core LangChain library as 'Plan-and-Execute,' highlighting its significance and utility in the field. Users can run the prompting methods with OpenAI API keys, either individually or with multiple keys for faster inference, and explore various prompt types like CoT, PS, and PS+ with different trigger sentences.

Best used for

Ideal for researchers and developers who need to implement and test advanced prompting techniques, improve LLM reasoning, and experiment with different chain-of-thought strategies. Especially valuable for academic research in NLP and developing sophisticated AI reasoning systems.

Common actions

improve reasoning

engineer prompts

experiment with LLMs

implement research

low-code/no-codedeepfakeautomated workflowcollaborationopen-source"AI Agents"face swappinggithub copilotworkflows

Capabilities

Key features

Zero-shot chain-of-thought
Multiple prompt types
OpenAI API integration
Multi-threaded inference
ACL 2023 paper code

Target Audience

professordeveloperresearcher

Integrations

openai

Pricing & Plans

Open Source

Free

FAQs

What is the core idea behind Plan-and-Solve Prompting?

Plan-and-Solve Prompting enhances large language models' zero-shot chain-of-thought reasoning by first devising a plan and then executing it step-by-step. This structured approach helps models tackle complex problems more effectively, leading to improved accuracy and logical coherence in their responses.

How can I run Plan-and-Solve Prompting?

You can run the prompting methods by setting your OpenAI API key(s) in the `apikeys.json` file. The project provides Python scripts (`main.py` for single-threaded and `main_threads.py` for multi-threaded inference) that allow you to specify the prompt ID, dataset, engine, and learning type.

What types of prompts are available in this framework?

The framework includes several prompt types, such as CoT (Chain-of-Thought), PS (Plan-and-Solve), and various PS+ variations. Each type uses a specific trigger sentence to guide the LLM's reasoning process, ranging from simple step-by-step thinking to detailed planning and variable extraction.

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Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra

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