Current Limitations in AI Document Retrieval

Traditional document retrieval methods often deliver too much information to language models, causing several critical problems:

• Token Inefficiency: Unnecessary content consumes valuable tokens, increasing API costs
• Context Window Limitations: Large documents can exceed model context windows
• Reduced Accuracy: Irrelevant information can confuse models and lead to poorer responses
• Slower Performance: Processing excessive data increases latency in AI applications

These challenges become particularly acute when building retrieval-augmented generation (RAG) systems that need to efficiently process company documentation, knowledge bases, or other large text collections. Without intelligent compression, organizations waste resources and compromise application performance.