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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.

Use system messages effectively to set context and reduce the need for repeated instructions.

Anthropic's Claude excels at following complex, multi-step instructions with high fidelity.

Use delimiters like triple quotes (''' '''), triple backticks (``` ```), or XML tags (<example></example>) to clearly separate instructions from the content you want the model to process. This helps prevent the model from getting confused.

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.

When prompting an LLM, be as clear and specific as possible. Define the desired format, length, tone, and role for the AI. For example, instead of 'Write about dogs,' try 'Write a 500-word blog post about the benefits of adopting a rescue dog, in a friendly and informative tone.'

When prompting an LLM, be as clear and specific as possible. Define the desired format, length, tone, and role for the AI. For example, instead of 'Write about dogs,' try 'Write a 500-word blog post about the benefits of adopting a rescue dog, in a friendly and informative tone.'

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.

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.

Start your prompt with a clear instruction like 'Translate the following text to French:' before providing the text itself. This primes the model for the task.

If incorporating user input into prompts, be aware of prompt injection vulnerabilities. Sanitize user inputs or use techniques to separate instructions from user data.