RAG Flow Diagram (2026): How Retrieval‑Augmented Generation Works End‑to‑End
By Mohit Agarwal, Paath.online7 min read
If you’ve built a RAG project once, you know it’s not “just embeddings + a prompt.” A real RAG system has ingestion, indexing, retrieval, re-ranking, prompt assembly, and evaluation. Here’s a clear flow diagram showing how it works.
How to Read This Diagram
- Ingestion (left): your knowledge base is built once, then refreshed when docs change.
- Retrieval (middle): for each query, you fetch candidate chunks and select the best few.
- Generation (right): the LLM answers using the selected context, ideally with citations.
Vector RAG vs Vectorless RAG (Where It Fits)
Notice the “Embed (optional)” box. If you skip embeddings and rely on keyword/BM25 + structured navigation, you’re doing vectorless RAG. If you include embeddings and vector search, you’re doing vector RAG. Many production systems are hybrid.
Read: Vector RAG vs Vectorless RAG (2026) →Want to build RAG with us?
At Paath.online, we teach RAG step‑by‑step: document parsing, chunking strategy, retrieval tuning, and evaluation—so students can build real Q&A apps.