Anatomy of a Basic Retrieval-Augmented Generation PipelineAnatomy of a Basic Retrieval-Augmented Generation Pipeline
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Fakhreddine JADIB
• Oct 7
Back to Basics: The Anatomy of a RAG Pipeline 🧱
Before diving into advanced techniques like hybrid search or agentic workflows, you have to master the foundation. This clean visualization perfectly breaks down the fundamental architecture of a Basic Retrieval-Augmented Generation (RAG) system.
Every solid RAG application is built on two distinct phases:
📦 Phase 1:
Data Indexing (The Prep Work) This is where you build your knowledge base. It involves taking raw documents (PDFs, Notion pages, databases), loading them, and splitting them into manageable Text Chunks. Those chunks are then passed through an embedding model to create Vector Embeddings, which are finally stored in a Vector DB (like Pinecone, Milvus, or pgvector). Pro-tip: Your chunking strategy here will make or break your entire system.
âš¡ Phase 2:
Data Retrieval & Generation (The Runtime) When a user asks a question, the system converts that specific query into a vector embedding using the same model from Phase 1. It then performs a similarity search against the Vector DB to retrieve the Top-K Chunks of relevant context. Finally, both the original query and the retrieved context are fed into the LLM to generate a highly accurate, context-aware response.
While simple, getting this baseline architecture right is the hardest part for most teams. If your embedding model is weak or your chunk size is wrong, no amount of advanced reranking will save the output.
What is your current go-to embedding model for setting up this foundational pipeline? Are you team OpenAI, Cohere, or using open-source models via HuggingFace? Let me know below! 👇
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