A technical tutorial breaking down the five-stage pipeline of RAG (Retrieval-Augmented Generation) was widely shared among developers this week: LLM applications are comprehensively shifting to "open-book exams," making this an unavoidable implementation standard for enterprises.

What this is

LLMs have two inherent flaws: their knowledge is frozen at the completion of training, and they don't know your company's internal rules. The core idea of RAG is to give LLMs an open-book exam—when a user asks a question, it first retrieves the most relevant document snippets from the enterprise knowledge base, then feeds these materials along with the question to the model, allowing it to answer with reference materials in hand.

This tutorial reveals that RAG is not a single technology, but an engineering pipeline comprising five stages: document loading, document chunking, generating Embeddings (converting text into computable mathematical vectors), storing them in a vector database (a database specifically for storing and retrieving vectors), and finally performing similarity search and generating an answer. These five stages are tightly linked; if any single link fails, the LLM's answers will go off track.

Industry view

We note that the industry consensus on RAG is shifting from "whether to use it" to "how to use it well." It directly solves the data privacy and hallucination issues enterprises care about most, without requiring massive investment to retrain models, making it economically efficient.

But it is worth our concern that RAG is not a silver bullet. The accuracy of the retrieval stage is the biggest hidden worry: if the materials retrieved from the knowledge base are incorrect to begin with, the LLM will only generate severe hallucinations based on wrong data. Currently, many dissenting voices point out that many teams underestimate the engineering difficulty, assuming that connecting a vector database solves everything. In reality, the dirty work—how to chunk documents, how fine to cut them, and how to handle hybrid search and reranking—is what truly determines the success or failure of the system.

Impact on regular people

  • For enterprise IT: The focus is shifting from "which LLM to choose" to "how to revitalize internal data." Historical data cleaning and document structuring have become the new cost bottlenecks.
  • For individual careers: The dividend period for "wrapper" developers who only know how to call APIs is ending. AI engineers who understand document chunking strategies and retrieval optimization are becoming an enterprise necessity.
  • For the consumer market: Various AI assistants will move beyond generic answers, and "personal external brain" products based on personal knowledge bases and proprietary data will become increasingly common.