1. Write Clear Instructions: To obtain precise responses, your prompts should be clear and specific. If you require brief replies, ask explicitly. For complex answers, request expert-level writing.
   • Tactics include specifying desired output length, including details for relevance, and demonstrating preferred formats.
2. Provide Reference Text: Language models can generate more accurate responses when supplemented with reference text.
   • Tactics involve instructing the model to use the reference text for answers or to include citations from it.
3. Split Complex Tasks into Simpler Subtasks: Breaking down complex tasks into smaller, more manageable ones can reduce errors and improve outcomes.
   • Tactics include using intent classification, summarizing previous dialogues, and recursive document summarization.
4. Give the Model Time to ‘Think’: Encouraging the model to reason out answers can lead to more accurate responses.
   • Tactics involve asking the model to work through solutions before concluding, and using inner monologue or sequential queries to guide reasoning.
5. Use External Tools: Supplementing the model’s capabilities with external tools can enhance its performance.
   • Tactics include using embeddings-based search and code execution for accurate calculations or API calls.
6. Test Changes Systematically: Evaluating changes with a comprehensive test suite ensures that modifications lead to overall performance improvements.
   • Tactics involve model outputs evaluation with reference to gold-standard answers.

Each strategy is accompanied by specific tactics to implement these methods effectively. The guide encourages experimentation to discover the most effective approaches for different tasks and scenarios.

Read the complete article here: https://platform.openai.com/docs/guides/prompt-engineering/strategy-test-changes-systematically

For more details and examples, the guide refers to the OpenAI Cookbook, which includes additional resources and tools for prompt engineering.