Retrieval Finds Candidates. Reranking Finds the Right One.

A hiring analogy that finally makes RAG Reranking click First, What Is RAG? Before we get into the analogy, let me give you a 30 second crash course on RAG because this is where reranking lives. RAG stands for Retrieval Augmented Generation. Here's the problem it solves: Large Language Models (LLMs) like GPT or Claude are incredibly powerful but they only know what they were trained on. They don't know about your company's internal documents, last week's product update, or your customer support knowledge base. RAG fixes that by giving the LLM a memory it can search. Here's how it works in three simple steps: Retrieve — When a user asks a question, the system searches your document library and pulls the most relevant chunks Augment — Those retrieved chunks are added to the prompt as context Generate — The LLM reads the context and generates a grounded, accurate answer Think of it like an open book exam. The LLM doesn't have to memorize everything it just needs to find the right page and