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When you need a specific output format, include a concrete example in your prompt rather than just describing it. Showing the model 'Output should look like: {name: string, score: number, summary: string}' with a filled example is far more effective than verbal descriptions alone.

For complex tasks, don't try to write the perfect prompt in one go. Start with a simple instruction, see the result, and then iteratively add more constraints, context, and examples to refine the output until it meets your requirements. This is more effective than starting with an overly complex prompt.

When asking an LLM to perform a task with a specific format, provide a few examples (2-3) in your prompt. This 'few-shot' approach helps the model understand the desired input-output pattern, leading to more consistent and accurate results.

Provide a few examples (input/output pairs) in your prompt to guide the LLM's response. This is called few-shot prompting and can significantly improve performance on specific tasks.

Use delimiters like triple backticks (```), XML tags (<tag></tag>), or quotes ("") to clearly separate different parts of your prompt, such as instructions, context, examples, and input data.

For intricate tasks, consider a structured prompt with sections like: ROLE, CONTEXT, TASK, OUTPUT_FORMAT, EXAMPLES. This organization helps the LLM understand requirements better.

To generate more detailed and nuanced content, first ask for a brief outline, then ask the LLM to expand on each point, and then ask it to add specific examples or data. This is a form of 'Chain of Density'.