Summary and Quiz

Congratulations! You’ve learned the fundamentals of generative AI. Let’s review what we have learned in this chapter and then take a quiz to validate the learning.

Summary

Let’s summarize the chapter on Fundamentals of generative AI:

• GenAI: The kind of artificial intelligence used for generating content, whether it is text, speech, images, or videos, is referred to as GenAI. The fundamental models that are used for GenAI are as follows:

  • Transformer-based models: The models that are based on transformer architecture that have an encoder and a decoder along with a self-attention mechanism to process and relate information across different parts of input data simultaneously, enabling efficient handling of complex patterns in language, vision, and other sequence-based tasks.

  • LLMs: These are applications of transformer-based models but on a much higher scale. They are fine-tuned for NLP-related applications.

  • Multi-model models: These models, often based on transformers, are designed to handle multiple types of input data—such as text, images, audio, or video—and can produce diverse outputs across these modalities, enabling cross-modal understanding and generation. This capability makes them versatile for applications like image captioning, video analysis, and text-to-speech conversion.

  • Diffusion models: These generative models learn to generate data by reversing a gradual noise-adding process. They start with pure noise and iteratively refine it to produce realistic data, such as images. The model “denoises” the input through a series of steps, generating high-quality samples that resemble the training data, making them popular for applications like image and video synthesis.

• Prompt engineering: The practice of creating precise and structured prompts to guide generative AI models, leading to relevant and accurate responses. Key components include:

  • Context: It includes relevant background information in a prompt, helping the model focus on a specific domain, like healthcare or finance, to produce a more tailored response.

  • Instruction: These are clear directions within the prompt specifying what the model should achieve, such as a particular tone or style, ensuring responses align with the user’s intent.

  • Model latent space: It refers to the internal “map” of relationships the model uses to understand and generate responses.

  • Specificity: It is the detail within a prompt that enhances response relevance. Specific prompts like "Discuss ethical challenges in healthcare AI" result in focused answers compared to broader requests.

  • Temperature and max tokens: These are parameters that control response style; temperature manages creativity, and max tokens limit length. Lower temperatures lead to factual answers, while higher ones yield more varied responses.