RAG

RAG augments LLM generation by retrieving relevant documents from an external knowledge base and injecting them into the input context at inference time ^[114]. Its non-parametric nature (Context Injection) and Version-Persistent coordinate confirm that RAG strictly does not modify underlying model parameters: the injected knowledge alters the model's transient output without touching its foundational weights. This fundamentally distinguishes RAG from Knowledge Editing, which permanently rewrites factual associations within the weight space itself. The information source is a separately maintained, indexable knowledge repository whose content is decoupled from the model's weights and can be updated independently (External Corpus)—documents can be added, revised, or removed from the corpus. This decoupling is the sharpest structural contrast with CPT, which internalizes domain knowledge directly into model weights (Parametric Update, Scheduled Permanent, Unbounded Cumulative): CPT makes knowledge intrinsic and persistent at the cost of a full retraining cycle for every knowledge update; RAG makes knowledge external and updateable at inference time at the cost of retrieval latency and context-window occupation. The compound Knowledge Update, Task Specialization profile reflects the dual function: the retrieval mechanism enables the model to answer with temporally current information (Knowledge Update) while the retrieved context shapes the generation to the specific domain and task at hand (Task Specialization). RAG functions as a complement to FT rather than a direct substitute: FT encodes structural behaviors and stylistic patterns directly into parameters, while RAG retrieves localized factual context from an external corpus. From a system engineering and validation perspective, the retrieval corpus constitutes a critical external data dependency. Consequently, the quality, currency, and provenance of the vector database must be rigorously version-controlled; updating the retrieval corpus constitutes a significant system-level configuration change that dictates the model's factual operational envelope and demands end-to-end integration testing, even when the core model parameters remain entirely unmodified.