1 link tagged with all of: token optimization + caveman compression + prompting + llm + context compression
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Caveman Compression strips predictable words like articles, fillers, and connectives to cut token count by 15–58% while preserving facts. It offers three methods—rule-based NLP, masked-LM, and LLM-based—to balance speed, cost, and compression ratio. Use it to pack more content into limited token budgets, especially for LLM reasoning and RAG contexts.
- Three compression modes trade off cost/speed for ratio: rule-based NLP (15-30%, <100ms, free), RoBERTa MLM (20-30%, 1-5s, free), and LLM-based via OpenAI API (40-58%, ~2s, needs API key)
- Benchmarks across 13 test cases show 100% fact preservation with ~40% average token reduction
- Compressed RAG contexts can store 2-3x more information, and agents reasoning directly in caveman format fit more chain-of-thought steps per context window
- Best suited for internal docs and agent reasoning, not for user-facing, legal, or emotional text where tone and grammar matter