•1 min read•from Towards Data Science
RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
Our take
In the evolving landscape of LLM systems, relying solely on Retrieval-Augmented Generation (RAG) is insufficient. As context expands, challenges arise that traditional tutorials often overlook. This article introduces a comprehensive context engineering system, developed in pure Python, designed to enhance memory management, compression, re-ranking, and token budgets. By addressing these critical aspects, it ensures that LLMs remain stable and effective under real-world constraints. Join us in exploring this innovative solution that bridges the gap, empowering developers to harness the full potential of LLM technology.
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.
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