Designing and Extending a Self‑Built ChatGPT System: Architecture, Session Management, and Scaling Strategies

ChatGPT is an LLM‑based dialogue system. This article describes how to build a similar system, covering the model, inference engine, and overall architecture.

The core conversation flow consists of a web front‑end that interacts with users, a server that receives requests and forwards them to an inference runtime, which loads the LLM, generates a reply, and returns it to the user, forming a basic framework.

Because the inference runtime is stateless, it lacks session and history management. To address this, a database component is added to store user sessions and message history, and a set of REST APIs is designed:

POST /chat : start a new session.

POST /chat/:chatID/completion : continue a conversation in an existing session.

GET /chats : retrieve the list of sessions.

DELETE /chat/:chatID : delete a specific session.

The stored message schema includes userID , chatID , userMessage , and assistantMessage . The server sends a unified prompt to the inference runtime, for example:

[
    {
        "role": "system",
        "content": "You are a helpful assistant."
    },
    {
        "role": "user",
        "content": "Hello!"
    },
    {
        "role": "assistant",
        "content": "Hello there, how may I assist you today?"
    },
    {
        "role": "user",
        "content": "How are you?"
    }
]

In the prompt, role distinguishes participants: system sets the background, user represents user input, and assistant denotes model output.

Historical messages can be handled in several ways: directly appending them to the prompt (suitable for short histories), dynamically truncating older messages due to LLM token limits, or summarizing past dialogue with the inference engine and inserting the summary into the prompt.

To improve scalability, a cache layer can be introduced to avoid redundant inference. The cache maps a user's question to the AI's reply, using semantic similarity via an embedding runtime and vector storage/search. Cache scope (per‑user vs. global) must be considered for privacy.

Elastic scaling is achieved by keeping the server stateless and adding a gateway for load balancing. The inference runtime, also stateless, can be horizontally scaled, though it consumes more resources. Introducing a message queue (MQ) enables asynchronous processing, enhancing resilience under high load.

For production readiness, the following steps are recommended: choose a database (e.g., PostgreSQL or MongoDB) and an inference engine (e.g., llama.cpp, HuggingFace Transformers, vLLM); add observability (logs, traces, metrics, alerts); set up CI/CD pipelines and deploy on a cloud platform using Kubernetes.

In summary, the article presents a complete design of a self‑built ChatGPT system, from basic conversation flow and session management to advanced extensions such as caching, elastic scaling, and production‑grade deployment.