3 tips to easily save 80% of Token consumption

Have you ever felt that Token consumption is ridiculously fast? Clearly, you haven't chatted for long, and your quota is almost depleted.

This is not your fault. Because big model companies want you to spend more tokens, so the current solutions are the most token-consuming.

But as long as you understand how AI operates, three small adjustments can cut consumption by more than half.

1: Don't let AI "browse" the web

This is the biggest source of waste.

Many people get 50+ web summaries of hot topics every day. The approach is to let AI control the browser to gather information, thinking it's convenient. But what's the cost? When AI browses web pages, it reads the entire content of the page — including text and even HTML code, all counted as tokens. A typical news page can use thousands of tokens, while you really only need a few hundred words.

This waste particularly amplifies for those who collect information and summarize hot topics daily.

The correct approach is very simple: if you can adjust APIs, do so; for regular webpages, write a crawler script. The script only grabs the fields you need, keeping it clean, with token usage practically zero.

2: Provide the file path to Subagent, don't "relay" information

Those using Agent workflows are most likely to fall into this pit.

In the current solutions, the main Agent reads all information and then distributes it to the Subagent. The problem is — all this information passes through the main Agent's context, even if entirely unrelated to the current subtask, it still counts as tokens.

The solution is intuitive: write the data into a file and only tell the Subagent the file path, letting it read the necessary parts itself. The main Agent's context remains concise, dramatically reducing token usage.

3: AI is for thinking, not for being an Excel

→ **Filter big data with code first** — don't dump everything onto AI all at once. Use a Python script for statistics and filtering to extract the most critical parts before letting AI make judgments and decisions.

Why are these three tips effective?

It's because fundamentally, AI is not human; it has no memory. For each request, you need to package and send the entire historical dialogue, tool definitions, skills, MCP configurations... Every time you chat longer, the package gets bigger, and tokens keep rising. Of course, big model companies won’t actively teach you how to save — the more you use it, the more they earn.

How to implement it?

Making changes is very easy; just use scripts more during the Skill phase — complex steps that can be done with code should be encapsulated in scripts, leaving only the segments that truly require "thinking" for AI. It's simple to execute these steps in the Skill as code; the other processes remain the same.

This method also has an additional benefit that many people may not realize: **while saving tokens, AI's accuracy may actually increase**.

The reasoning is simple — the shorter the context, the less noise, making it easier for AI to focus on key information and make correct judgments. Too long of a context can lead to "distraction" and hallucinations.

Saving money and improving quality are actually the same thing.

If you find this useful, share it with your friends who also use AI, and feel free to give a three-part thank you!

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