Generative AI 'CARD'

In the context of this course, a card refers to a structured format or template used to define a specific task or objective for generating content or output using generative AI techniques.

Card Model

• A model of a generative AI task is a conceptual framework that describes the structure, components, and relationships involved in generating content or output using generative AI techniques.

• It provides a high-level abstraction of the task, capturing essential elements and defining their interactions.

Here's a simplified model of a generative AI task:

1. Objective: The overarching goal or purpose of the generative AI task. This defines what needs to be achieved through the content generation process.

2. Input: Information provided to the generative AI technique to guide the content generation process. This includes:

   • Prompt: A starting point or stimulus to generate content. It could be a partial sentence, a keyword, or any other form of input that triggers the generation process.

   • Context: Additional information or constraints that provide context for the generation task. This may include background knowledge, relevant data sources, or specific requirements.

3. Generative Model: The AI model responsible for generating content based on the input provided. This could be a pre-trained language model such as GPT-3, a neural network architecture designed for text generation, or any other generative AI system.

4. Output: The generated content produced by the generative model in response to the input. This includes:

   • Generated Text: The actual output generated by the AI model, which could be in the form of text, images, or other media depending on the task.

   • Evaluation Metrics: Criteria used to assess the quality and relevance of the generated content. This may include measures of coherence, relevance, grammaticality, and other factors depending on the specific task requirements.

5. Feedback Loop: A mechanism for iteratively improving the generative AI model based on feedback from users or evaluators. This may involve refining the input prompts, adjusting model parameters, or incorporating additional training data to enhance performance.

Overall, the model of a generative AI task helps define the key components involved in the task and their relationships, facilitating the design, execution, and evaluation of generative AI tasks in various applications.

Card Instance

From the described model of a generative AI task (CARD), we can derive the following example (instance):

1. Objective:

   • Description: The overarching goal or purpose of the generative AI task.
   • Example: "Generate a creative story based on a given prompt."

2. Input:

   • Prompt: A starting point or stimulus given to the model to generate content.
   • Context: Additional information or constraints that provide context for the generation task.
   • Example:
     • Prompt: "Once upon a time, in a magical kingdom..."
     • Context: "The story should include elements of fantasy and adventure."

3. Generative Model:

   • Description: The AI model responsible for generating content based on the input provided.
   • Example: GPT-3 (Generative Pre-trained Transformer 3).

4. Output:

   • Generated Text: The actual output generated by the AI model in response to the input.
   • Evaluation Metrics: Criteria used to assess the quality and relevance of the generated content.
   • Example:
     • Generated Text: "Once upon a time, in a magical kingdom far away, there lived a brave young knight named Arthur..."
     • Evaluation Metrics: Coherence, relevance to the prompt, grammaticality.

5. Feedback Loop:

   • Description: A mechanism for iteratively improving the generative AI model based on feedback.
   • Example: Incorporating user feedback to refine the input prompts.