RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work

# What Actually Makes AI Systems Reliable at Work Most companies try to plug AI systems into their existing data and hope for the best—but the real bottleneck isn't getting the AI to find information, it's managing what information to actually show it so it doesn't get confused or break. A developer built a practical system that acts like a smart filter, deciding what context to keep, what to compress, and how much to feed the AI at once, so the system stays stable even when dealing with huge amounts of company data.
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, and token budgets — so LLMs stay stable under real constraints. The post RAG Isn’t
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