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Tell the model what NOT to do in addition to what you want. For example, 'Explain quantum computing without using analogies or metaphors.' Negative constraints often produce more focused and accurate outputs than positive instructions alone.

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.

To guide the model away from undesired outputs, tell it what *not* to do. For example, add constraints like 'Do not use technical jargon,' 'Avoid mentioning price,' or 'The response should not be longer than two paragraphs.'

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.'

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.

Start your prompt by telling the model to act as an expert persona. For example, 'Act as a senior copywriter specializing in luxury brands.' This primes the model to adopt the desired tone, style, and domain knowledge, leading to higher-quality outputs.

The 'temperature' parameter controls randomness. Lower values (e.g., 0.2) make output more deterministic and focused. Higher values (e.g., 0.8) increase creativity and diversity. Adjust it based on your task.

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.

Start the desired output for the LLM. For instance, if you want a list, start your prompt with the beginning of the list: 'Here are the steps:
1.' This can guide the model effectively.

Adding constraints to your prompt (e.g., word count, specific keywords to include/exclude, format requirements) can often lead to more focused and useful outputs from the LLM.

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

Use stop sequences to tell the model when to stop generating text. This is useful for preventing run-on sentences or irrelevant content after the desired output.

Ask the LLM to review its own previous output for errors or areas of improvement. 'Review your previous response. Are there any inaccuracies or ways to make it clearer?'

For complex tasks, chain multiple prompts together. The output of one LLM call becomes the input (or part of the input) for the next. This allows for sophisticated workflows.

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.

Clearly list all constraints the output must satisfy. For example, 'Write a poem about a cat that is exactly 10 lines long and mentions the moon.'

For complex tasks, reveal information progressively. Start with basic requirements, get initial output, then add more specific constraints or details.