The article „Prompt Engineering“ provides a comprehensive guide on optimizing the outcomes from large language models like GPT-4. It introduces specific strategies and tactics, some of which can be combined to increase effectiveness. The article emphasizes the importance of experimenting to find the best methods for individual needs.
Key Points of the Article:
- Give Clear Instructions: Models can’t read minds, so it’s crucial to provide precise instructions. Too long or too simple answers can be adjusted through specific instructions. Tactics include incorporating details in requests, setting a persona for the model, and using delimiters to clearly separate different parts of an input.
- Provide Reference Texts: Since models sometimes invent incorrect answers, especially on esoteric topics, providing reference texts can improve accuracy.
- Break Complex Tasks into Simpler Subtasks: Complex tasks often lead to higher error rates. Breaking them down into simpler steps can enhance accuracy.
- Allow Models Time to ‚Think‘: Similar to human thinking, models can provide more accurate results when given time to contemplate an answer. A ‚Chain of Thought‘ query before the final answer can be helpful.
- Use External Tools: Combining the model with other tools, such as text search systems or code execution engines, can improve performance.
- Systematically Test Changes: To ensure that changes have a positive impact, they should be tested against a representative set of examples.
The article also includes many practical examples and links to additional resources, which aid in understanding and applying the described tactics.
Here you can find the complete article from Open AI.
