The Art of Prompt Engineering: Crafting Inputs for Desired AI Outputs

Prompt engineering is the process of carefully crafting the prompts and inputs to AI models like GPT-3.5 in order to get the desired high-quality outputs. It involves tweaking parameters like wording, context, temperature, max tokens, and task decomposition.


  • Prompt engineering is about finding the right way to frame tasks and questions to get the AI model to produce the desired response.
  • It often requires iterative experimentation by making small changes to prompts and analyzing model outputs.
  • Key aspects include specificity, providing relevant context, specifying output format/structure, adjusting temperature and max tokens, and breaking down complex tasks.
  • Feedback loops and continuously refining prompts based on model responses is important.
  • Prompt engineering patterns like conditional reasoning, providing examples, conversational formatting etc. can be used.
  • The article links to resources on basics of prompt engineering and game prompt patterns.
  • Other articles recommend clear syntax in prompts, guide structured prompt engineering for ChatGPT, and classify prompt patterns into categories.


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