TokenSlim | Local Prompt Compressor & LLM Token Minifier

1. Verbose System Prompt

Chars: {{ rawPromptText.length }} Est. Tokens: {{ rawTokenCount }}

2. Compression Rules

Prune Fillers Remove polite words ("please") and passive modals.

XML / Tag Packing Compress context introductions into micro XML tags.

Prune Spacing Collapse repetitive line breaks and extra tabs.

Convert Shorthand Translate structures (e.g. "under no condition") to "never".

Compressed Tokens {{ compressedTokenCount }}

Tokens Saved {{ tokenDelta }}

Shrinkage Rate {{ compressionPercentage }}%

3. Compressed Prompt

{{ compressedPromptText || 'Empty prompt input...' }}

4. Estimated Savings (per 1M API Calls)

Claude 3.5 Sonnet Saved: ${{ calculatedSavings.sonnet }} Input rate $3/M

GPT-4o Saved: ${{ calculatedSavings.gpt4o }} Input rate $2.50/M

GPT-4o Mini Saved: ${{ calculatedSavings.mini }} Input rate $0.15/M

How LLMs Parse Compressed Prompts

Advanced generative models process syntax based on token embeddings, not grammatical elegance. Verbose structural frames and polite transition clauses are stripped to save computation space while maintaining instruction mapping accuracy.

1. The Cost of Empty Tokens

Every word, space, and delimiter in your system prompt costs money upon execution. By compressing redundant introductory sentences into short, structured XML identifiers, you free up active context space for the model to generate longer, more precise outputs.

2. Instruction Strength Preservation

Replacing conditional statements like "It is highly important that you make sure to avoid..." with direct, imperative keywords like "Never" reduces token consumption while actually strengthening the strictness of the guardrails.