A practical guide to reducing LLM token costs in RAG and agentic pipelines using two context compression strategies: extraction-based (LLMChainExtractor) and selection-based (LLMChainFilter) via LangChain. Covers precise token counting with TikToken, implementing both approaches against the same FAISS-backed retrieval pipeline, comparing token reduction percentages, and calculating real-dollar savings at scale. Also addresses best practices like avoiding over-compression, caching compressed results, layering selection before extraction for maximum reduction, and handling production edge cases.
Table of contents
How to Optimize Token Usage with Context CompressionTable of ContentsWhy Token Optimization Matters NowHow Context Windows Drain Your BudgetExtraction vs. Selection: When to Use WhichImplementing Context Compression with LangChainMeasuring Real-Dollar SavingsBest Practices and PitfallsNext Steps155 Impressions