My users are complaining that the 5-10 second wait for a full response is too slow for a mobile app. I've enabled streaming, but the UI still feels "choppy." Are there specific front-end libraries or Redis-based caching strategies that can make the experience feel instantaneous?
3 answers
Streaming is only half the battle; you need to handle the "First Token Latency." We use a Redis-based semantic cache (like GPTCache). If a user asks a question similar to one asked in the last hour, we serve the cached answer in milliseconds. For the UI, we use the ai package from Vercel, which handles the stream-to-text conversion seamlessly in React/Next.js. Also, avoid heavy post-processing on the stream. If you must format the text, do it on the client-side as the tokens arrive. This makes the app feel like it's "thinking" in real-time rather than lagging.
Does caching help with personalized questions, or is it only useful for generic FAQs? I’m worried about serving one user's private data to another.
Try using gpt-4o-mini for the initial "acknowledgment" or simple tasks. It has a much lower latency than the full GPT-4o model and can make the app feel faster.
Great point, Cynthia. We use a small model to say "Sure, let me look that up for you..." while the bigger model is doing the heavy lifting in the background.
That’s a critical security concern, Matthew. You must include a user_id or session_id in your cache key to prevent cross-contamination. For personalized apps, we don't cache the full response; we cache "Entity Extractions." For example, if the agent already knows the user's name and preferences, those are cached in a fast SQL store and injected into every prompt. This reduces the time ChatGPT spends "remembering" who it's talking to, which shaves off a few hundred milliseconds from every single turn.