Many are already familiar with OpenAI’s six prompt optimization strategies, but is there a tool that can apply these strategies to optimize prompts? Yes, there is! Introducing auto-openai-prompter, an open-source project developed by LangChain founder Harrison Chase.
Project Overview
The project’s main goal is to feed OpenAI’s six prompt optimization strategies to GPT models, then harness GPT-4’s powerful generation capabilities to assist users in optimizing their prompts. This can be seen as a strategy for winning with intelligence.
Project URL: https://github.com/hwchase17/auto-openai-prompter
Overcoming Prompt Engineering Challenges
Application-level prompt writing can be quite challenging, requiring developers to break away from conventional programming patterns and make good use of the large model’s capabilities. This has become a sticking point for many developers.
The open-source project also includes a testing playground that doesn’t require an OPENAI_API_KEY. After testing it, the results are excellent – it can automatically distinguish between SYSTEM PROMPT and USER INSTRUCTION based on OpenAI’s six strategies. This is something many developers have been dreaming of achieving. If you have the ability to deploy this open-source project, it may open up some ideas for commercialization.
Diving into the Project’s Main Prompt
Let’s take a closer look at the main prompt for the auto-openai-prompter project:
Prompt engineering
This guide shares strategies and tactics for getting better results from large language models (sometimes referred to as GPT models) like GPT-4. The methods described here can sometimes be deployed in combination for greater effect. We encourage experimentation to find the methods that work best for you.
Some of the examples demonstrated here currently work only with our most capable model, gpt-4. In general, if you find that a model fails at a task and a more capable model is available, it's often worth trying again with the more capable model.
You can also explore example prompts which showcase what our models are capable of:
Prompt examples
Explore prompt examples to learn what GPT models can do
Six strategies for getting better results
Write clear instructions
These models can't read your mind. If outputs are too long, ask for brief replies. If outputs are too simple, ask for expert-level writing. If you dislike the format, demonstrate the format you'd like to see. The less the model has to guess at what you want, the more likely you'll get it.
Tactics:
Include details in your query to get more relevant answers
Ask the model to adopt a persona
Use delimiters to clearly indicate distinct parts of the input
Specify the steps required to complete a task
Provide examples
Specify the desired length of the output
Provide reference text
Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. In the same way that a sheet of notes can help a student do better on a test, providing reference text to these models can help in answering with fewer fabrications.
...Key Takeaways from the Project’s Main Prompt
A few lines from the project’s main prompt are particularly worth reflecting on:
- “These models can’t read your mind.” The models can’t guess what you want.
- “The less the model has to guess at what you want, the more likely you’ll get it.” Clearer instructions lead to better results.
- “Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs.” LLMs may fabricate information, so be cautious.
Conclusion
The auto-openai-prompter project showcases an innovative approach to optimizing prompts by combining OpenAI’s strategies with the power of GPT-4. It has the potential to be a game-changer for developers working on prompt engineering and LLM applications.
By leveraging GPT-4’s generation abilities, developers can supercharge their prompts and overcome common prompt engineering challenges. Testing the playground with real-world examples demonstrates the tool’s effectiveness in distinguishing between system and user prompts.
As you explore this open-source project, keep in mind the key takeaways from its main prompt to guide your own prompt optimization efforts. With tools like auto-openai-prompter, the possibilities for commercialization and enhanced human-AI interaction are vast. It’s an exciting time for developers to dive into prompt engineering and unlock the full potential of large language models.
