Information Overload: The AI Performance Bottleneck

When building AI applications, sending too much irrelevant data to language models creates serious problems:

• Increased costs: Processing unnecessary text wastes tokens and computing resources
• Reduced accuracy: Excess information dilutes relevant context, leading to poorer responses
• Slower performance: Processing large documents causes noticeable delays in user experience
• Context limitations: LLMs have maximum token limits that prevent processing entire document libraries

Traditional search and retrieval methods often send entire documents or large chunks without understanding what's truly relevant to a specific query, forcing your AI to wade through mountains of irrelevant text.