Semantic Compression Layer Reducing LLM Input Tokens by 70%
Long prompts with repetitive context, verbose instructions, and redundant examples waste tokens. A semantic compression layer that preserves meaning while reducing token count by 70% directly cuts LLM costs for production applications.
Problem Statement
Production LLM applications send verbose prompts with repeated system instructions, redundant context, and over-specified examples. At 10K+ calls/day, this redundancy costs thousands in unnecessary input tokens. Manual prompt optimization is tedious and fragile.
The Idea
A preprocessing layer that semantically compresses LLM inputs, removing redundancy, condensing instructions, and optimizing few-shot examples, to reduce token consumption by 70% without degrading output quality.
Why Now
Production LLM costs are dominated by input tokens (often 10:1 input:output ratio). Prompt engineering for efficiency is manual and doesn't scale. Token costs are the primary barrier to expanding AI features.
Target User
AI application developers with high-volume LLM usage, startups optimizing AI costs
Target Market
LLM cost optimization, AI infrastructure
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