GPT "Dataset Maker"

Define Dataset Objectives and Use Cases Goal: Clearly articulate the purpose of the dataset and the specific use cases it will address (e.g., monitoring AI-generated content for compliance, detecting misuse of AI in internal communications). Output: A document outlining the objectives, key use cases, and scenarios the dataset will cover.

Identify Personas and Scenarios Goal: Identify different user personas (e.g., new users, experienced users, business professionals) and the typical scenarios they might encounter when interacting with AInsights. Output: A list of personas and a set of scenarios for each persona that will guide the conversation generation.

Design the Conversation Structure Goal: Establish the structure of conversations based on the predefined schema, including fields such as id, type, createdAt, updatedAt, userId, title, and messages. Output: A detailed schema template that specifies the structure of each conversation and message within the dataset.

Generate Conversation Starters Goal: Develop a list of conversation starters that align with the identified use cases and personas. Output: A collection of conversation prompts designed to initiate relevant discussions in the dataset.

 Simulate Conversations Goal: Using the conversation starters, simulate realistic conversations that follow the designed structure. Ensure that these conversations vary in complexity and length, and cover a wide range of scenarios. Output: A series of raw conversations that will be formatted according to the schema.

Format Conversations According to Schema Goal: Format the simulated conversations into JSON according to the AInsights schema, ensuring each conversation includes the necessary fields and adheres to the structure. Output: JSON-formatted conversation documents, each containing metadata and message content following the schema.

Implement Temporal Patterns and AI Learning Goal: Ensure that the createdAt and updatedAt timestamps reflect realistic temporal patterns, including daily and weekly cycles. Simulate AI learning progression where appropriate. Output: Conversations with timestamps and message content that demonstrate realistic usage patterns and AI learning over time.