In this week’s?newsletter, you'll find?me going on a deep dive into "prompt engineering", or as one professor poetically called it, "programming in prose".
Prompt engineering is the action of instructing an LLM model, like that underlying ChatGPT, how to accurately generate the results you want, whilst minimising some of the nasty side-effects like "hallucinations" when the model just makes things up.
The article is useful for anyone who plans on using (or is using), ChatGPT for writing, including content marketers and SEO experts and wants to up their AI game.
I provide a clear strategy and the steps required to create accurate content for professional projects using ChatGPT.
If that sounds of interest to you, go check it out.
And now, here's the TL;DR
- LLMs, like the models underpinning ChatGPT, have experienced rapid adoption and are undergoing integration into mainstream applications such as Google Workspace and Microsoft Office.
- Prompting is a crucial skill for utilising AI effectively, so long as you use it in a professional way providing context and direction for AI-generated content.
- To optimise LLM outputs, users can adjust model settings like temperature and Top P, which control the confidence and probability of token predictions.
- A good prompt for content generation requires components like context, input data, instructions, and output format.
- Iterate with your language model, instead of trying to dump a mega-prompt on it and hoping it will figure out everything in one go (it might, or might not)
- Setting the context is essential and often includes providing background information and assigning a role to the AI.
- Provide input data relevant to the task, such as audience or buyer avatars for content creation projects.
- Specify the desired output format, which can range from blog posts to code snippets.
- Be aware of model limitations, such as hallucinations or token context window constraints.
- Utilise techniques like Chain of Thought (CoT) prompting to improve the accuracy and performance of language models.
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