Token Pruning

Strip filler words, compress verbose phrases, and trim conversation history windows. Every pruned token is money saved.

Every unnecessary token costs money

The Problem

LLM pricing is per-token. Filler words ('I would really appreciate if you could please'), verbose phrases ('in order to' instead of 'to'), and uncompressed conversation history all add tokens that carry no semantic value. Across thousands of requests, these add up to real money.

The Solution

Tokonomy's pruning engine applies layered strategies: whitespace cleanup, filler word removal, verbose phrase compression, conversation history sliding window, and system prompt optimization. Only text content is touched. Code, JSON, tool definitions, and structured data are never modified.

Before and After

Before Pruning

I would really appreciate if you could please carefully look at this function and maybe tell me what you think might possibly be causing the issue that we have been experiencing lately ~42 tokens

After Pruning

Look at this function and identify the issue ~10 tokens

Token savings: 76%

How It Works

Text content blocks in the request are analyzed

Filler words and verbose phrases are identified and compressed

Conversation history is windowed to keep only relevant recent context

Code blocks, tool definitions, and structured data pass through unchanged

Frequently Asked Questions

Is pruning the same as compression?

Pruning is one layer of the compression engine. It handles rule-based optimizations (filler removal, phrase compression). Full compression adds LLM-powered rewriting on top.

Will pruning remove important words?

No. Pruning targets known filler patterns and verbose phrases. Content words, technical terms, and instructions are preserved.

Does it work on system prompts?

Yes. System prompts often contain the most verbose language and benefit significantly from pruning.

Can I control the aggressiveness?

Yes. Compression profiles range from Low (12%) to High (72%). Pruning intensity scales with the profile.

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