Design ChatGPT - 10-Minute Sprint

What is ChatGPT?

ChatGPT is an advanced conversational application built on a sophisticated artificial intelligence language model developed by OpenAI. It is designed to understand natural language and generate human-like text, making it a highly effective tool for various communication tasks. Clear and effective communication is crucial in many situations, such as explaining complex concepts, brainstorming ideas, or writing professional content. In these cases, ChatGPT can help by:

• Simplifying technical subjects.

• Proposing innovative ideas.

• Composing clear, well-structured text.

ChatGPT-like systems are particularly noteworthy because they can interpret various inputs, even when prompts are incomplete or ambiguous. They can respond to complex, abstract, or open-ended questions with clarity and relevance. These features work because the model is trained on massive datasets using generative AI and language modeling techniques that help it understand and produce natural responses. Rather than relying on predefined answers, such systems analyze context, identify patterns in language, and generate responses that align with the intent and structure of the input.

In this lesson, we’ll design a text-to-text generation system similar to ChatGPT. While OpenAI has not publicly disclosed ChatGPT’s exact architecture and design details, the approach covered in this lesson is based on general principles, established techniques, and best practices commonly used in building such systems.

Let’s start by defining the core requirements, which play a critical role in shaping the overall design:

Requirements

Building the backend of a text-to-text generation system like ChatGPT requires careful attention to both functional and nonfunctional requirements:

Functional requirements

• Dialogue management: The system must enable context-aware, text-based conversations by interpreting user input, maintaining context, and generating relevant, human-like responses.

• Natural language understanding: The system must interpret user input by identifying intent, extracting key entities, and understanding the context.

• Personalization: The system should adapt to user preferences and provide personalized recommendations based on previous interactions.  

• Feedback: The system should improve its accuracy and performance over time based on feedback.

Nonfunctional requirements

• Scalability: ...