Comparing RAG, SKILL, MCP, and RLM Architectures for LLMs

On November 13, 2026, at 17:12, the developer platform Juejin published an article titled "RAG vs. SKILL, MCP, and RLM Architectures: A Comparative Analysis," originally written by Alex Ewerlöf and compiled by Yue Yang. The piece opens with a premise that LLMs are generalists: whether they are base models, instruction-tuned models, or reasoning models, all of them run into capability limits when they are applied to specialized, domain-specific tasks. The author frames the core question as how developers can push past that ceiling and extend what a generalist LLM can reliably accomplish.

To answer that question, the article takes a close look at four architectural approaches: RAG, SKILL, MCP, and RLM. It walks through the distinct emphasis each one places on extending model capabilities and spells out the scenarios in which each approach is better suited. The comparison is intended as a practical reference, helping developers decide which architecture to adopt when the demands of a task differ. By laying out the trade-offs side by side rather than advocating for a single option, the piece positions the four schemes as complementary tools rather than direct rivals.

At present, this topic is documented only through this single comparative write-up. No follow-up reports, additional commentary, or further developments have been published since the article went live, so the discussion currently rests at the stage of this initial architectural overview.