Prompt engineering

Prompt engineering is the practice of designing inputs for large language models (LLMs) and other generative AI (genAI) tools. Successful prompt engineering refers to effective prompts that produce desired outputs. 

The practice demands creativity and inventiveness, as users who are dissatisfied with the output of their LLM must reconsider their original prompt, editing it for specificity and clarity. Prompt engineering is a step-by-step process, and users use output to refine input. Prompt engineering can involve text-to-text models, text-to-image models, or text-to-audio models.

A text-to-text model will transform text input into a text response, and a text-to-image model will produce an AI-generated image. Current text-to-image models include DALL-E 2, Midjourney, and Stable Diffusion. Text-to-image models might be particularly useful to architects, building designers, and visual designers.

Though prompt engineering can produce works of art and design, the practice is arguably an art form itself. A blog post from Google Cloud provided advice to developers looking to improve and leverage their prompt engineering skills. Like other outlets, the Google Cloud blog encouraged specificity:

Let’s say you would like your AI model to generate a recipe for 50 vegan blueberry muffins. If you prompt the model with ‘What is a recipe for blueberry muffins?’, the model does not know that you need to make 50 muffins. It is thus unlikely to list the larger volume of ingredients you’ll need or include tips to help you more efficiently bake such a large number of muffins. The model can only go off the context that is provided. A more effective prompt would be “I am hosting 50 guests. Generate a recipe for 50 blueberry muffins.” The model is more likely to generate a response that is relevant to your request and meets your specific requirements.

Fine-tuning prompt engineering produces better and more specific results, thus enabling the user’s mission – whether it’s related to speechwriting, arithmetic reasoning, or another task. Prompt engineers should also remain aware of prompt injection attacks from malicious users. 

Prompt engineering can produce the following business benefits:

  • A better understanding of the generative AI model and its capabilities 
  • Fewer machine learning biases 
  • Finer marketing copy and increased quality control 
  • Faster resolution of customer issues and increased customer satisfaction with the company’s AI chatbot
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