📚 Excited to share insights from my recent read! 🌟 David Clinton's "The Complete Obsolete Guide to Generative AI" from Manning Publications has been an eye-opener, especially diving into the second chapter.
Ever wondered about the key parameters shaping an AI model? This book delves deep into configuring them to match specific needs. Parameters like temperature, Top P value, frequency penalty, and presence penalty play a pivotal role in fine-tuning output.
Understanding and tweaking these settings can significantly impact ChatGPT's output. Setting parameters enables tailoring the output, whether you seek a more deterministic response closely linked to the input or desire a more creative and diverse output.
To get hands-on, we'll simulate a scenario extracted from my book Data Storytelling with Generative AI using Python and Altair.
Read the full article here 👇👇👇
https://towardsdatascience.com/how-to-improve-your-chatgpt-outputs-using-configuration-parameters-0eebd575646e
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