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DataCamp Tutorial on Prompt EngineeringSelf-made image. Tutorial cover.

Its been a week and in ForCodeSake we still have emotional hangover!

Last Friday 28th of July, we run our first ForCodeSake online tutorial on Prompt Engineering. The tutorial was organized by DataCamp as part of their series of webinars about GPT models.

As the first online debut of ForCodeSake, we decided to show different techniques to optimize the queries when using GPT models in Data Science or when building powered-LLM applications. Concretely, the tutorial had three main goals:

Learn the principles of Good Prompting.

Learn how to standardize and test the quality of your prompts at scale.

Learn how to moderate AI responses to ensure quality.

Feeling like you would like to follow the tutorial too?In this short article, we aim to provide the pointers to the courses material for you to benefit from the full experience.

To follow the webinar, you need to have an active OpenAI account with access to the API and generate an OpenAI API Key. No idea where to start? Then the following article is for your!

Have you ever received lackluster responses from ChatGPT?

Before solely attributing it to the models performance, have you considered the role your prompts play in determining the quality of the outputs?

GPT models have showcased mind-blowing performance across a wide range of applications. However, the quality of the models completion doesnt solely depend on the model itself; it also depends on the quality of the given prompt.

The secret to obtaining the best possible completion from the model lies in understanding how GPT models interpret user input and generate responses, enabling you to craft your prompt accordingly.

More:

Optimizing GPT Prompts for Data Science | by Andrea Valenzuela ... - Medium