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If you need a concise summary or a detailed explanation, specify the desired length (e.g., 'in one paragraph,' 'in 100 words,' 'provide a comprehensive overview'). This guides the LLM's output.

If you want diverse ideas or solutions, ask the LLM to generate several options (e.g., 'Provide 3 different headlines for this article.'). This gives you more to choose from and refine.

When providing text for an LLM to process (e.g., summarize, translate), use clear delimiters like triple quotes ('''Text here''') or XML tags (<text>Text here</text>) to separate your instructions from the content.

For complex problems, break them into smaller, manageable steps. Use an LLM for each step, then combine the results. This is often more effective than one large, complicated prompt.

Zero-shot prompting asks an LLM to perform a task without prior examples. Few-shot prompting provides 1-5 examples within the prompt, often significantly improving performance on novel tasks.

If you need structured output (e.g., JSON, XML, a list), explicitly ask the LLM to provide it in that format. This often yields better and more parsable results. Example: 'Provide your answer as a JSON object with keys 'name' and 'summary'.'

Don't expect the perfect response on your first try. Prompt engineering is an iterative process. Refine your prompts based on the LLM's output to improve results. Small changes can make a big difference.

Treat your prompts as code. Use version control (like Git) to track changes, iterate, and collaborate on prompt engineering.

Challenge: Create the most effective prompt for a complex task using the fewest possible tokens. Test different approaches and measure both output quality and token efficiency.