Around the page resources, business APIs and streaming responses of AI applications, cache, access links and connection strategies are planned respectively to allow users to start and complete an interaction more smoothly.
AI applications have both reusable static resources and dynamic responses that rely on user identity and context. Waiting may occur in the network, retrieval, queuing or model generation stages. It is necessary to split the time consumption first and then formulate strategies for different paths.
Configure caching of page scripts, styles, and public images, and use versioned paths to manage updates. Attachments and private files with permissions need to be verified separately for authentication and caching behavior.
Question and answer, session history, and user task results are processed according to dynamic interfaces, and shared caching is disabled in combination with response headers and edge rules to prevent different users from reading the same private results.
Interfaces using SSE need to check proxy buffering, idle timeout and connection behavior, check whether the first output can arrive in time, whether subsequent content continues to be returned, and verify midway cancellation.
Observe the time spent on page loading, connection, business processing, retrieval, model queuing and generation separately. Access acceleration improves network paths, and model calculation and queue bottlenecks need to be handled by the application and computing power layers.
Distinguish between reads that can be safely retried and requests that create tasks, and use task identification and idempotent mechanisms to avoid repeated generation or deduction of business usage after connection interruption.
Compare first-part responses, complete results, outages, and error rates from major user regions, and test performance when cache updates and origin server anomalies occur.
List resource domain names, Q&A and upload interfaces, SSE or WebSocket usage, and record user regions and current time-consuming baselines.
Configure HTTPS and static cache, check dynamic interfaces, streaming connections, request size and timeout requirements, and verify authentication and cancellation behaviors.
Start with a portion of traffic. Compare latency, errors and origin load, while retaining the previous endpoint and a DNS rollback plan.
Continue to pay attention to page loading, end-to-end first token delay, streaming completion rate, reconnection ratio and origin server load, and use application-side indicators to locate remaining bottlenecks.
Network and resource delivery can be optimized, but model execution, retrieval, and queuing are still determined by the backend. Network waiting and generation time should be measured separately; model-side bottlenecks require adjustment of inference resources, context, or scheduling.
You should first provide the actual interface and maximum interaction time, and check the streaming forwarding, buffering and timeout support of the selected service. Verify piece-by-piece output, idle connections, cancellation and reconnection before going online; WebSocket support does not mean verified SSE.
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