rag retrieval strategy

Persistent knowledge graphs are an evolution of RAG, addressing its statelessness limitation. This is a direct extension improving retrieval efficiency.

RAG results are explicitly mentioned as one component of context engineering that must be balanced against other information sources.

Semantic memory retrieval pattern is functionally equivalent to RAG: external knowledge stored separately, retrieved based on query/task, injected into prompt.

RAG retrieval is listed as one of the components managed within the context lifecycle in context engineering

MCP servers can implement RAG-like retrieval patterns. The protocol standardizes how retrieval systems expose context to LLMs.

LlamaIndex is a primary RAG framework; context engineering relates directly to what context gets retrieved and how it's prioritized.

MCP can be viewed as a protocol layer above RAG—standardizing how agents retrieve and request context from knowledge sources.

MCP provides architectural foundation for how retrieval systems expose relevant context to AI—moves beyond prompt-level RAG to protocol-level context exposure

Article explicitly mentions 'retrieve the most relevant' information, identifying retrieval as critical component of context engineering architecture

Just-in-time data fetching vs upfront metadata dumps is fundamentally a retrieval strategy question—when and how to fetch context for agent use.

MCP servers effectively implement RAG infrastructure at the protocol level, giving models standardized way to retrieve external knowledge without custom SDK integration.

Cron jobs updating documentation/skill files function similarly to RAG refresh cycles—ensuring agents have fresh, relevant context to retrieve from.

Memory files are retrieved and used as context in future sessions—a RAG pattern where user's own work becomes the retrieval corpus.

Automated context assembly from metadata layer resembles RAG approach of retrieving relevant context before inference