This video is a recorded session (Session 14) focusing on generative AI, specifically using the IBM Watson X platform. The instructor covers various topics, including prompt engineering techniques (zero-shot, one-shot, and few-shot prompting), model training for text generation, code generation, and information extraction, as well as feedback and project classification. The session includes practical demonstrations and coding examples.
Prompt Engineering Techniques: The session details zero-shot, one-shot, and few-shot prompting methods, demonstrating how to effectively interact with AI models using different levels of example input.
Model Training and Functionality: The instructor explains the process of training AI models on the IBM Watson X platform, focusing on text generation, code generation, information extraction, feedback classification, and project classification. The functionalities of the platform are demonstrated.
Tokenization and Word Embeddings: The importance of tokenization and word embeddings in AI model processing is explained, showing how the models analyze and understand text input.
Project and Feedback Classification: The session showcases the use of free-form and structured methods in classifying projects and feedback, highlighting the differences in approach and how each works with the IBM Watson X platform. Specific examples of training data are provided.
Coding Examples (with caveats): Python code snippets are presented to illustrate zero-shot and one-shot prompting using different models (Helen-key-NLP and GPT2). Note that the transcript indicates some issues with the Hugging Face library during the coding demonstration.
