---
type: concept
title: "Traditional RAG"
description: "The classic one-shot, static RAG pipeline that embeds a query, runs a single vector similarity search, and stuffs the top chunks into the prompt."
tags: [rag, naive-rag, vector-search, baseline]
videos: [i-love-the-karpathy-llm-wiki-but-it-doesnt-scale-heres-what-does, why-the-best-ai-coding-tools-abandoned-rag-and-what-they-use-instead, the-simplest-rag-stack-that-actually-works-complete-guide, knowledge-graphs-in-n8n-are-finally-here, introducing-rag-20-agentic-rag-knowledge-graphs-free-template, make-rag-100x-better-with-real-time-knowledge-graphs, i-built-the-ultimate-rag-mcp-server-for-ai-coding-better-than-context7, the-proven-solution-for-unbelievable-rag-performance-lightrag-guide, the-ultimate-n8n-rag-ai-agent-template-local-ai-edition, the-future-of-rag-is-agentic-learn-this-strategy-now]
created: 2026-07-21
updated: 2026-07-21
---

# Traditional RAG

Traditional RAG (also "naive" or "basic" RAG) is the original, one-shot retrieval pipeline: embed the query, run a single vector similarity search, and stuff the top chunks into the prompt. Cole names it precisely as the thing critics mean when they say RAG is dead: "When people say rag is dead, they are specifically referring to what I like to call traditional rag or semantic search where we have chunks from our documents and then we embed those into a vector database" (why-ai-coding-abandoned-rag, 0:01:04). The pipeline is simple by design, "you take your documents, you split them into bite-sized chunks, use what is called an embedding model to create the vector representation, store that in a vector database" (rag-20, 0:07:13).

## The one-shot limitation

The defining constraint is that retrieval happens exactly once, with no chance to reconsider. "The problem with all of this is it is considered a one shot, you look up information in the vector database and you do it only one time and there's only ever the opportunity to do it once" (n8n-local-edition, 0:02:57). The agent is stuck with whatever came back: "our agent has to use that context, whether it likes it or not" (rag-20, 0:08:19), and "there's no opportunity for the agent to actually improve upon whatever it got for that additional context" (future-of-rag-agentic, 0:04:28).

## Why it falls short

Traditional RAG is inflexible and brittle at scale. It struggles with specificity, "classic rag will often fail with this because if you have a lot of different years worth of data in your knowledge base, it might find 2023 revenue instead of 2025 because it's more of that conceptual search" (the-simplest-rag-stack, 0:08:33). Accuracy numbers can be poor, "some benchmarks give basic rag 75% accuracy while others cite as low as 35 to 45%" (lightrag guide, 0:00:34), and it degrades with volume, "if you have thousands or hundreds of thousands of documents that's when normal rag really starts to fall apart" (lightrag guide, 0:13:11). It is also static: keeping the vector store in sync with changing data "is inefficient and unreliable" (make-rag-100x-better, 0:00:16). A structural weakness is that vector databases "don't do a good job storing relationships between the different entities that we have in our data" (knowledge-graphs-n8n, 0:01:33).

> **Contradiction:** Cole calls traditional RAG the target of "RAG is dead" claims and enumerates its failures, yet elsewhere insists RAG "is still as relevant as ever" (see [When RAG Is (and Isn't) Dead](./when-rag-is-dead.md)). The resolution he lands on: traditional one-shot RAG is what falls short, while RAG broadly (agentic, hybrid, graph-augmented) remains the backbone. This is an evolving view, not a flat contradiction.

## Contrasts with

- [Agentic RAG](./agentic-rag.md) is the direct successor: it lets the agent reason about, refine, and re-run retrieval instead of accepting a single shot.
- [Knowledge Graphs](./knowledge-graphs.md) address the relationship-storage gap that vector-only traditional RAG cannot.
- [Temporal-Aware Knowledge Graphs](./temporal-knowledge-graphs.md) - a knowledge graph that never overwrites a changed fact but stores the new one alongside the old with valid_at/invalidated_at timestamps, so an agent knows both what is true now and how the knowledge evolved.
- [When RAG Is (and Isn't) Dead](./when-rag-is-dead.md) frames the whole debate this page sits inside.
- [Production-Ready RAG](./production-ready-rag.md) - Building RAG that survives a growing knowledge base, updated documents, and unexpected user questions, unlike toy 'chat with your PDF' demos.

## Builds on

- [Semantic Search](./semantic-search.md) is traditional RAG's single retrieval mechanism.
- [Vector Embeddings](./vector-embeddings.md) and [Chunking](./chunking.md) are the ingredients that make it work.
- [Retrieval-Augmented Generation (RAG)](./rag.md) is the umbrella; traditional RAG is its simplest instance.

## Related

- [Hybrid Search](./hybrid-search.md), [Contextual Retrieval](./contextual-retrieval.md), and [Late Chunking](./late-chunking.md) are upgrades that patch traditional RAG's weak spots.
- [Memory Systems](./memory-systems.md) still lean on traditional vector RAG where it scales best: "we are using vectors here. So, it's more traditional rag with semantic search. That way we're able to scale and each individual user can have millions of memories" (karpathy-wiki-doesnt-scale, 0:15:31).

## Sources

- [Why the Best AI Coding Tools Abandoned RAG (And What They Use Instead)](../sources/why-the-best-ai-coding-tools-abandoned-rag-and-what-they-use-instead.md) - "[0:01:04] When people say rag is dead, they are specifically referring to what I like to call traditional rag or semantic search where we have chunks from our documents and then we embed those into a vector database."
- [The ULTIMATE n8n RAG AI Agent Template - Local AI Edition](../sources/the-ultimate-n8n-rag-ai-agent-template-local-ai-edition.md) - "[0:02:57] the problem with all of this is it is considered a one shot you look up information in the vector database and you do it only one time and there's only ever the opportunity to do it once"
- [Introducing RAG 2.0: Agentic RAG + Knowledge Graphs (FREE Template)](../sources/introducing-rag-20-agentic-rag-knowledge-graphs-free-template.md) - "[0:08:19] the reason why naive rag is almost never enough is because it is extremely inflexible our agent has to use that context, whether it likes it or not."
- [The Simplest RAG Stack That Actually Works (Complete Guide)](../sources/the-simplest-rag-stack-that-actually-works-complete-guide.md) - "[0:08:33] classic rag will often fail with this because if you have a lot of different years worth of data in your knowledge base, it might find 2023 revenue instead of 2025 because it's more of that conceptual search"
- [The PROVEN Solution for Unbelievable RAG Performance (LightRAG Guide)](../sources/the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md) - "[0:13:11] if you have thousands or hundreds of thousands of documents that's when normal rag really starts to fall apart"
- [Make RAG 100x Better with Real-Time Knowledge Graphs](../sources/make-rag-100x-better-with-real-time-knowledge-graphs.md) - "[0:00:16] rag is very static it is your responsibility to constantly keep the agents knowledge base in sync with your data store. And that process is inefficient. and unreliable."
- [Knowledge Graphs in n8n are FINALLY Here!](../sources/knowledge-graphs-in-n8n-are-finally-here.md) - "[0:01:33] the main problem that we have with vector databases here, as great as they are, is they don't do a good job storing relationships between the different entities that we have in our data"
- [The Future of RAG is Agentic - Learn this Strategy NOW](../sources/the-future-of-rag-is-agentic-learn-this-strategy-now.md) - "[0:04:28] there's no opportunity for the agent to actually improve upon whatever it got for that additional context"
- [I Love the Karpathy LLM Wiki but it Doesn't Scale. Here's What Does.](../sources/i-love-the-karpathy-llm-wiki-but-it-doesnt-scale-heres-what-does.md) - "[0:15:31] we are using vectors here. So, it's more traditional rag with semantic search. That way we're able to scale and each individual user can have millions of memories and we're still going to be able to pull out the most important ones"
- [I Built the Ultimate RAG MCP Server for AI Coding (Better than Context7)](../sources/i-built-the-ultimate-rag-mcp-server-for-ai-coding-better-than-context7.md) - "[0:15:36] Right now it's more of a traditional rag lookup whenever it searches the private knowledge base that we have built out."
