Lossless Compression Techniques Can Boost RAG Agent Performance and Cut LLM Costs

Large Language Models operate within a fixed token limit called a context window, which poses a challenge for Retrieval Augmented Generation (RAG) agents that must inject retrieved documents into that space. When retrieved content exceeds the context limit, agents typically resort to truncation or summarization, both of which risk losing critical information or introducing inaccuracies. Lossless compression offers an alternative by reducing the token footprint of retrieved text without discarding any factual content, preserving full fidelity for accuracy-sensitive domains like law and medicine. Techniques such as keyword extraction, deduplication, semantic chunking, and syntactic simplification can be applied before or after retrieval to maximize information density. Beyond improving response quality and reducing hallucinations, these methods can also lower operational costs since most LLM APIs charge based on token usage.