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This is a practical guide on how to generate better AI outputs by up-leveling your inputs
Created by: The AI Exchange Team
A “prompt” is the input that guides a generative AI model to generate useful outputs. Generative AI tools like ChatGPT, GPT-3, DALL·E 2, Stable Diffusion, Midjourney, etc. all require prompting as their input.
In a natural language processing (NLP) context, “prompt engineering” is the process of discovering inputs that yield desirable or useful results. As is the story with any processes, better inputs yield better outputs; or commonly said another way “garbage in, garbage out.”
Designing effective and efficient prompts will increase the likelihood of receiving a response that is both favorable and contextual. With a good prompt, you can spend less time editing content and more time generating it.
As companies like PromptBase arise around the idea that the prompt is the “secret sauce” to using generative AI, prompt engineering could easily become the “career of the future.” But, any generative AI user can become an “advanced” prompt engineer. Here’s how:
The more time you spend asking ChatGPT questions and receiving responses, the better your idea will be of various prompting approaches and their individual strengths and weaknesses
Use Open AI’s GPT playground to perform interactive trial and error with variations in your prompt, model, temperature and top_p values (uniqueness of answer, i.e. creativity), and more available within the UI itself.