𝗛𝗼𝘄 𝗜 𝗙𝗶𝘅𝗲𝗱 𝗔𝗜 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗪𝗶𝘁𝗵 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗮𝗰𝗵𝗶𝗻𝗴
I built a chat assistant for a client. It worked poorly.
Users asked a question. They waited 15 seconds. They saw a blank screen. Then they left. The client was unhappy.
The problem was not the AI model. The problem was my code. I waited for the full response before showing anything to the user.
I tried several fixes. Async did not help. Caching exact text only worked for FAQs. Limiting token counts made answers useless.
I solved it using two methods.
- Streaming
Most AI APIs support streaming. Instead of waiting for the whole block of text, you get small chunks. You can show these chunks as they arrive.
The first word appears in 300ms. The full answer still takes time, but the user sees progress immediately. This keeps users engaged.
- Semantic Caching
Users often ask similar questions. I built a cache that understands meaning.
I use sentence embeddings and a vector database. Before calling the API, I check if a similar question exists in my cache.
If a match exists, I return the answer in 10ms. This removed the need for an API call for 30% of my users.
The Results:
• Streaming improves the user experience by showing real-time progress. • Semantic caching reduces costs and cuts latency for repeat questions.
The Trade-offs:
• Streaming makes your backend more complex. You must manage open connections. • Caching requires extra hardware or software like a vector database. • Setting cache thresholds is hard. If the threshold is too high, you miss matches. If it is too low, you give wrong answers.
Stop blaming the AI model for slow speeds. Look at how you handle the data.
Optional learning community: https://t.me/GyaanSetuAi