𝗜 𝗧𝗿𝗶𝗲𝗱 𝗧𝗼 𝗔𝗱𝗱 𝗔𝗜 𝗖𝗵𝗮𝘁 𝗧𝗼 𝗠𝘆 𝗔𝗽𝗽 𝗔𝗻𝗱 𝗛𝗶𝘁 𝗔 𝗪𝗮𝗹𝗹

I tried to add an AI chat assistant to my project management tool. I thought it would be easy. I planned to send the entire chat history to an API.

It failed.

After 15 messages, the responses became slow or broken. The API threw errors because the text was too long for the token limit.

Here is what I tried and what worked.

The Problems I Faced:

  • Truncating history: I kept only the last few messages. This fixed speed but the AI forgot everything from the start of the chat.
  • Summarization: I asked the AI to summarize the chat every 5 messages. This helped memory but increased my costs and wait times.
  • Vector stores: I tried scoring messages by relevance. This added too much complexity for my needs.

The Solution:

I stopped trying to send everything. I used two main methods to fix the experience.

  1. Streaming: I used Server-Sent Events to show text as it generates. This makes the app feel fast even if the AI takes time to think.

  2. A Three-Slot Context Window: I split my token budget into specific parts.

  • System Prompt: 500 tokens. This stays the same.
  • Dynamic Context: 2000 tokens. This holds recent project updates and task states.
  • Conversation History: 4000 tokens. This is a sliding window of recent messages.

By managing the budget this way, I reduced my payload size by 40%. This saved money and lowered latency.

My Advice:

Adding AI is not just about calling an API. You must manage how much data you send. Streaming improves how the user feels speed. A smart context strategy improves how smart the AI feels.

How do you manage conversation memory in your apps? Do you use sliding windows or summarization?

Source: https://dev.to/__c1b9e06dc90a7e0a676b/i-tried-to-add-ai-chat-to-my-app-and-hit-a-wall-with-context-tokens-459b

Optional learning community: https://t.me/GyaanSetuAi