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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.

To get structured data, explicitly ask the LLM to format its response as JSON, a markdown table, or a numbered list. For example: 'Compare the top 3 smartphones and provide the output in a markdown table with columns for Model, Price, and Key Feature.'

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

When using Claude, make use of its XML tags for better structure in your prompts.

For complex tasks involving multiple pieces of information, provide the input in a structured format (e.g., using JSON within the prompt, or clear headings) to help the LLM parse it correctly.

The quality of your LLM's output is highly dependent on the quality of your input prompt. Clear, well-structured, and relevant prompts lead to better results.

Structure prompts by defining: WHAT you want the LLM to do, WHY it's important (context), and HOW it should do it (format, style, constraints).