Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative AI model.[1][2] A prompt is natural language text describing the task that an AI should perform.[3]
A prompt for a text-to-text language model can be a query such as "what is Fermat's little theorem?",[4] a command such as "write a poem about leaves falling",[5] or a longer statement including context, instructions,[6] and conversation history. Prompt engineering may involve phrasing a query, specifying a style,[5] providing relevant context[7] or assigning a role to the AI such as "Act as a native French speaker".[8] A prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog),[9] an approach called few-shot learning.[10]
When communicating with a text-to-image or a text-to-audio model, a typical prompt is a description of a desired output such as "a high-quality photo of an astronaut riding a horse"[11] or "Lo-fi slow BPM electro chill with organic samples".[12] Prompting a text-to-image model may involve adding, removing, emphasizing and re-ordering words to achieve a desired subject, style,[1] layout, lighting,[13] and aesthetic.
Prompt engineering is the process of structuring words that can be interpreted and understood by a text-to-image model. Think of it as the language you need to speak in order to tell an AI model what to draw.
Prompt engineering is the art of communicating with a generative AI model.
We demonstrate language models can perform down-stream tasks in a zero-shot setting – without any parameter or architecture modification
what is the fermat's little theorem
"Basic prompt: 'Write a poem about leaves falling.' Better prompt: 'Write a poem in the style of Edgar Allan Poe about leaves falling.'
Next, I gave a more complicated prompt to attempt to throw MusicGen for a loop: "Lo-fi slow BPM electro chill with organic samples."