RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
RAG systems break when conversation history accumulates and context windows overflow. This post introduces a full context engineering layer built in pure Python that sits between retrieval and prompt construction. The system includes five components: a hybrid retriever blending TF-IDF and dense embeddings, a tag-weighted re-ranker, an exponential decay memory system with auto-importance scoring and deduplication, an extractive compressor with three strategies, and a slot-based token budget enforcer. Real benchmark numbers show naive RAG overflows a 800-token budget by 10 characters, while the full engine fits within budget using re-ranking, intelligent compression, and decay-filtered memory. Performance on CPU is ~92ms end-to-end in hybrid mode, with embedding generation as the bottleneck. The post also honestly documents design trade-offs including empirical alpha values, heuristic re-ranking weights, and missing features like persistent memory and cross-encoder re-ranking.
Table of contents
TL;DRThe Breaking Point of RAG SystemsWhat Context Engineering Actually IsWho This Is ForFull Pipeline ArchitectureComponent 1: The RetrieverComponent 2: The Re-rankerComponent 3: Memory with Exponential DecayToken Budget Under PressureComponent 4: Context CompressionComponent 5: The Token Budget EnforcerWhat Happens Under Real Token PressureMeasuring What It Actually Buys YouMemory Decay by Importance ScorePerformance CharacteristicsHonest Design DecisionsTrade-offs and What’s MissingClosingReferencesDisclosureSort: