Summarize Locally, Send Less: A Mobile LLM Pattern for Free-Tier APIs
Your mobile AI assistant has been chatting for ten minutes. Every turn, the client re-sends the entire conversation history to the server, and you have not noticed because the UI stays smooth. Then the error appears: quota exhausted. You check the logs and find that 80% of your token spend was repetition, not new information. This is the hidden cost of naive context management, and it becomes…
The report discusses a mobile LLM (Language Model) client pattern that reduces token waste and latency by summarizing its own conversation history before sending it to the server. This approach, called the On-Device Summarization Pattern, is particularly useful for free-tier APIs that impose strict token limitations, forcing developers to be more mindful of every token used.
The pattern involves splitting the conversation history into old and recent segments, summarizing the old segment locally, and sending the summary along with the recent messages. The summary can be created using extractive heuristics or more sophisticated local transformer models. The report provides a TypeScript implementation for this pattern, demonstrating how to extract the first sentence of each message for summarization.
This approach can significantly cut down on token usage, especially in longer conversations, while still maintaining relevant context.
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