5 Reranking Techniques in RAG: From Fast Retrieval to Accurate Context

source: OpenAI GPT Image 2 model You have built a RAG pipeline. You chunked your documents, picked an embedding model, and wired up a vector database. You ask a question, the retriever pulls back 50 chunks, and you stuff the top 5 into your LLM prompt. The answer is… okay. Not great. Sometimes it misses the mark entirely. You check the retrieved chunks. The one that actually contains the answer? It is sitting at position 23. Your embedding model thought it was moderately relevant, but not top-5 relevant. So your LLM never sees it. This is not a rare edge case...