Open AI’s Prompt Engineering guide

Here is a summary of their 6 strategies for getting better results when prompting GPT:

1. Write Clear Instructions:
– Be specific: Clarity in instructions leads to more relevant outcomes.
– Define the desired output length and complexity.
– Demonstrate preferred formats.
– Minimize ambiguity to enhance model accuracy.

2. Provide Reference Text:
– Counteract potential fabrications with concrete reference materials.
– Reference texts guide the model towards accurate and reliable answers.

3. Split Complex Tasks into Simpler Subtasks:
– Break down tasks to reduce errors and improve manageability.
– Consider tasks as workflows of simpler, interconnected steps.

4. Give the Model Time to “Think”:
– Allow the model to process and reason, similar to a human solving a complex problem.
– Encourage a “chain of thought” approach for more accurate reasoning.

5. Use External Tools:
– Supplement the model’s capabilities with specialized tools for specific tasks.
– Leverage resources like text retrieval systems or code execution engines.

6. Test Changes Systematically:
– Measure improvements with a comprehensive testing approach.
– Ensure that modifications lead to overall performance enhancements.

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