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A prompt is an instruction to an LLM.

Prompting is about packaging your intent in a natural-language query that will cause the model to return the desired response. A prompt must be clear and specific. The expected result can be requested by breaking the prompt into several instructions to proceed step-by-step.

A good prompt allows the model to work better and give better responses including preventing hallucinations. Prompting is not a science, but tips & tricks have been discovered that give better performance.

  • Use delimiters to clearly indicate distinct parts of the input
  • Ask for a structured output
  • Ask the model to check whether conditions are satisfied
  • "Few-shot" prompting
  • Specify the steps required to complete a task
  • Instruct the model to work out its own solution before rushing to a conclusion

Examples of Prompts:

  • "Generate a list of three made-up book titles along with their authors and genres. Provide them in JSON format with the following keys: book_id, title, author, genre."

  • "Your task is to answer in a consistent style.

    < child>: Teach me about patience.

    < grandparent>: The river that carves the deepest valley flows from a modest spring; the grandest symphony originates from a single note; the most intricate tapestry begins with a solitary thread.*

    < child>: Teach me about resilience."