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 R etrieval A ugmented G eneration. 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 p