RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
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RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
Most RAG tutorials focus on retrieval or prompting. The real problem starts when context grows. This article shows a full context engineering system built in pure Python that controls memory, compression, re-ranking,...
Editorial Analysis
RAG pipelines have become table stakes, but most implementations hit a wall once retrieval results exceed a few thousand tokens. The real bottleneck isn't fetching documents—it's deciding which ones matter and how to compress them without losing signal. I've seen teams spend months tuning their vector databases only to watch LLM outputs degrade under context bloat. This article's focus on context engineering as a distinct layer mirrors what we're building into our data platforms. You need explicit orchestration around memory management, semantic re-ranking, and progressive compression if you want production-grade reliability. The Python-first approach signals that these aren't research problems anymore—they're infrastructure problems that data engineers own. My recommendation: audit your current RAG implementations for context waste. Most teams are feeding LLMs 10x more tokens than necessary, burning costs and degrading quality. Building a proper context layer between your retrieval engine and your model isn't optional at scale.