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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.

Challenge: Prompt an LLM to explain a complex topic like quantum computing in simple terms a 5-year-old could understand. Evaluate its clarity and accuracy.

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.'

When asking for a summary, specify the goal. Do you need key takeaways, an abstract, a summary for a specific audience, or a certain length? This helps the LLM produce a more useful summary.

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.

Review your prompts for ambiguous words or phrases that could be interpreted in multiple ways. Strive for explicitness.

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.

If your prompt uses domain-specific jargon or acronyms the LLM might not know, provide a small glossary or definitions within the prompt.

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).

Generate user manuals, API documentation, and technical guides. Ensure clarity for your target audience's technical level.