New Prompt Engineering Guide

This guide presents effective strategies and techniques for optimizing results from large language models, often referred to as GPT models, such as GPT-4. The methods outlined here can be combined for enhanced effectiveness, and we encourage users to experiment to discover what works best for their specific needs.

Explore Example Prompts

To better understand the capabilities of GPT models, you can explore various example prompts that showcase their potential.

Six Strategies for Improved Results

1. Write Clear Instructions

Language models cannot read your mind. If you want concise outputs, request brief replies. For more complex responses, specify the desired depth of information. If the format isn’t to your liking, demonstrate the preferred structure. The clearer your instructions, the more accurate the model's responses will be. 

 

Tactics:

• Include specific details in your queries for more relevant answers.
• Ask the model to adopt a specific persona.
• Use delimiters to clearly indicate different parts of your input.
• Specify the steps needed to complete a task.
• Provide examples to guide the model.
• Indicate the desired length of the output.

2. Provide Reference Text

Language models can sometimes generate inaccurate information, especially on niche topics or when asked for citations. Just as students benefit from study notes, providing reference text can help models produce more accurate responses.  

 

Tactics:

• Instruct the model to base its answers on provided reference text.
• Ask the model to include citations from the reference material.

3. Split Complex Tasks into Simpler Subtasks

Similar to software engineering practices, breaking down complex tasks into manageable components can reduce error rates. Complex tasks can often be redefined as a series of simpler tasks, where the output of one task serves as the input for the next.  

 

Tactics:

• Use intent classification to identify relevant instructions for user queries.
• For lengthy dialogues, summarize or filter previous conversations.
• Summarize long documents in parts and compile a comprehensive summary.

4. Give the Model Time to "Think"

Just as humans may need time to solve a problem, models can also benefit from a moment to reason through their answers. Requesting a "chain of thought" can lead to more reliable conclusions. 

 

Tactics:

• Instruct the model to work through its solution before arriving at a final answer.
• Use inner monologue or a sequence of queries to reveal the model's reasoning process.
• Ask the model if it overlooked anything in previous attempts.

5. Use External Tools

To compensate for the model's limitations, consider integrating outputs from other tools. For instance, a text retrieval system can provide relevant documents, while a code execution engine can assist with calculations. Offloading tasks to specialized tools can enhance overall efficiency.  

 

Tactics:

• Implement embeddings-based search for efficient knowledge retrieval.
• Use code execution for accurate calculations or to call external APIs.
• Grant the model access to specific functions as needed.

6. Test Changes Systematically

Improving performance is easier when you can measure it. Sometimes, a modification may yield better results for a few examples but worsen overall performance. To ensure that changes are beneficial, define a comprehensive test suite. 

 

Tactic:

• Evaluate model outputs against gold-standard answers to assess performance.

Conclusion

Each of the strategies outlined above can be implemented with specific tactics designed to inspire experimentation. While these tactics provide a solid foundation, feel free to explore creative approaches not covered here. By refining your prompt engineering skills, you can significantly enhance the effectiveness of your interactions with large language models. 

 

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