AI Generation Process #AI #MachineLearning #PromptEngineering #GenerativeAI #AIDevelopment

Have you ever wondered why AR responses sound repetitive, too formal, or slightly robotic? Let's talk about how the response generation process works. When you type a prompt, the model first breaks it down into tokens. Tokens are small pieces of text, such as whole words or even parts of a word. For example, unhappy might be split into un and happy. Most models use a transformer architecture that understands how all the tokens relate to one another. Each of these tokens is then converted into a list of numbers called an embedding by the transformer. These embeddings capture the meaning of the token based on patterns to the model's training data. The model starts off with a word and using the embeddings created. The model predicts the next word to add to the output sequence and repeats this until the response is complete. The model doesn't plan what it's going to say ahead of time or know how the sentence will end when it starts. It simply detects patterns from the training data and uses probabilities to generate what most likely should go next. Kind of like the word suggestions you see when texting on your phone. A responses can feel unusual because the model generates a text step by step and a small mistake early on can shift the direction completely. When unsure, it may default to vague repetitive answers. Temperature settings control randomness where lower values make the output more predictable and higher ones increase variety and risk. The model's tone and style also depend on training, model type, and system settings. Today, we learned how AI builds responses one token at a time using probabilities. If you want to keep learning, AB has a free prompt engineering guided project on cognitive class where you can learn to use AI more effectively.

AI doesn’t think and compose answers the way humans do. Instead, it generates responses with tokens and probabilities. - AI models break down text into tokens and convert them into embeddings that capture meaning. - Each response is generated one token at a time, with the next token chosen as a prediction rather than part of a pre-planned answer. - Because of this step-by-step process, even small missteps early on can shift the overall tone of the response. Interested in learning how to use AI more effectively? Try IBM’s free Prompt Engineering course on Cognitive Class: https://ibm.biz/BdeMex #AI #MachineLearning #PromptEngineering #GenerativeAI #AIDevelopment