Self-Reflection Loops Explained | RAG for ML #19

Your RAG system generates an answer. But what if the answer is incomplete, vague, or misses a key part of the question? A self-reflection loop gives the model a chance to critique its own output and decide whether to improve it before the user ever sees it. In this episode we cover: Why a single generation pass is not always enough How self-reflection works as a post-generation step Building a critique prompt that identifies answer gaps Parsing the critique to decide whether to regenerate Running the reflection loop with a max iteration cap Combining reflection with retrieval for targeted improvement Next up: GraphRAG