rag-prompting-layer-not-architecture

IN premise — entries/2026/06/21/wiki-LLaMA-chunk-4.md

Created 2026-06-21T09:50:09+00:00

Retrieval-Augmented Generation (RAG) is a prompting-layer technique that combines retrieval from external knowledge with generation to reduce hallucination; it does not modify the model architecture.

Summary

RAG works by stuffing relevant documents into the prompt alongside the user's question, so the model has fresh context to lean on instead of inventing answers from memory. This matters because it means you can improve accuracy on any existing model without touching its training or architecture, but it also caps how much you can fix — the model still relies on whatever it already knows to reason over the retrieved text.

Dependents

These beliefs depend on this one:

IN rag-addresses-knowledge-currency-at-prompting-layer — RAG provides a prompting-layer solution to hallucination and knowledge freshness without architectural changes, operating entirely at the context level to augment model outputs with dynamically retrieved information.