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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.

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 reduce hallucinations, explicitly instruct the LLM to say 'I don't know' or a similar phrase if it cannot answer a question confidently or accurately based on the provided context.

When processing long documents, try to place key instructions or questions at the beginning or end of the input text. Models sometimes pay more attention to the start/end of context.

Understand the context window limit of the model you are using. Information outside this window will be ignored. For long interactions, summarization or RAG is key.

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).

When dealing with sensitive topics, instruct the LLM to consider emotional context: 'Respond with empathy and understanding, considering the emotional state of someone facing this situation.'