Intent Normalization

Trunkate AI goes beyond simple substring matching. It uses a sophisticated Intent Normalization pipeline to understand the core structural goal of your prompt and strip away conversational fluff.

The Normalization Pipeline

When you send a prompt to the Trunkate optimizer, it undergoes a deterministic multi-stage process.

Removing Politeness and Filler

LLMs don’t need “Please” or “Kindly” to function. The first stage of normalization aggressively strips these tokens, saving on average 2-5% of prompt budgets on chatbot interactions alone.

Standardizing Instructions

We recognize common prompt engineering patterns (“Think step by step”, “You are a helpful assistant”) and standardize them into their most token-efficient forms based on the target model’s tokenizer.

Context Preservation

The core meaning of the prompt is extracted and preserved. Only the “wrapper” language is modified.

Economic Implications

By integrating Intent Normalization into your pipeline:

1. Lower Costs: Reduce prompt tokens which directly lowers API bills.
2. Faster TTFT: Smaller prompts mean faster Time-To-First-Token latency.
3. Consistent Quality: The semantic meaning remains unchanged, protecting the quality of your LLM outputs.