Design of a ChatGPT System
Explore the design of a ChatGPT-like system, delving into its key components and the workflow.
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So far, we’ve identified the requirements, estimated storage needs, and outlined the foundational components for designing a ChatGPT-like system. Now, we’ll move into its System Design to understand how these components work together and how it ensures real-time, context-aware conversations.
High-level design of ChatGPT
A system as complex as ChatGPT requires a well-structured design to handle real-time conversations efficiently. The high-level design offers a bird’s-eye view of how components interact to form a cohesive system.
The workflow for the high-level design is provided below:
The user submits a text prompt through the interface or an API.
The API gateway receives the request and handles authentication, rate limiting, and session management before forwarding the prompt to the model server for processing.
The AI model processes the provided prompt and uses the conversation history to generate a response. These responses are stored in the cache to quickly retrieve repeated or similar requests and saved in the database for logging, analytics, or future reference.
The final response is sent back to the user via the API gateway.
User feedback is collected and stored to help improve system performance and guide future model updates or retraining.
Did you know that every piece of feedback you provide helps ChatGPT grow smarter? It is used to fine-tune the system by enhancing its ability to generate more accurate and natural responses.
Curious about how this process works? Dive deeper into “System Design of a Text-to-Text Generation System.”
Is a typical cache (like LRU or TTL-based) sufficient for storing AI-generated responses?