---
type: source
title: "The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)"
description: "Contextual retrieval (contextual embedding) is the single most impactful, easy-to-implement upgrade to basic RAG, prepending an LLM-generated situating sentence to each chunk before embedding to dramatically cut retrieval failure rates."
youtube_id: z_LGan-t2Mo
url: https://www.youtube.com/watch?v=z_LGan-t2Mo
slug: the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide
published: 2025-05-07
duration: "0:26:23"
recency_rank: 94
raw: "../raw/the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide.md"
tags: [rag, contextual-retrieval, chunking, n8n]
created: 2026-07-21
updated: 2026-07-21
---

# The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)

Cole walks through why basic RAG is unreliable (Anthropic's data shows ~10% retrieval failure with plain embedding) and how contextual retrieval, introduced by Anthropic, fixes it. The core technique, contextual embedding, is to run each chunk plus the full document through a cheap LLM with a simple prompt that produces one or two sentences situating the chunk within the document, then prepend that context (separated by a triple-dash) before embedding. This helps the model 'zoom out' and understand how each retrieved chunk fits the larger knowledge base, reducing RAG failure rate by up to 35% on its own and, when combined with other strategies, from ~10% down to under 3%.

He builds the pipeline live in n8n: Google Drive trigger, per-file loop, delete-existing-records for a clean slate, text extraction, a custom JavaScript chunker (400 characters, no overlap), a per-chunk LLM call (GPT-4.1 Nano) using Anthropic's exact prompt, and insertion into a Neon Postgres vector store with the prepended context plus metadata (file ID, title, URL) for source citation. Cost is managed via prompt caching (repeated document tokens are heavily discounted, ~50% on OpenAI, ~90% on Anthropic/Gemini) and by using a tiny cheap model since little reasoning is needed. The AI agent side stays a basic single-tool RAG lookup limited to the top four chunks, but the retrieved chunks are now far more useful.

He closes by noting the same technique is implemented in Python inside his Crawl4AI RAG MCP server (self-hosted Supabase, also Postgres), which he plans to fold into Archon, and teases further strategies (BM25 hybrid search, reranking, query expansion, agentic RAG) that stack on top of contextual retrieval for even higher accuracy.

## Concepts covered

- [Contextual Retrieval](../concepts/contextual-retrieval.md)
- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Chunking](../concepts/chunking.md)
- [Prompt Caching](../concepts/prompt-caching.md)
- [Model Selection](../concepts/model-selection.md)
- [Hybrid Search](../concepts/hybrid-search.md)
- [Reranking](../concepts/reranking.md)
- [Semantic Search](../concepts/semantic-search.md)
- [Vector Embeddings](../concepts/vector-embeddings.md)
- [Stacking Context-Engineering Strategies](../concepts/strategy-stacking.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [n8n](../entities/tools/n8n.md)
- [Neon](../entities/tools/neon.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [OpenAI](../entities/organizations/openai.md)
- [MCP (Model Context Protocol)](../entities/tools/mcp.md)
- [Archon](../entities/tools/archon.md)
- [Dynamous](../entities/organizations/dynamous.md)

## Key moments

- **[0:00:00]** Intro: contextual retrieval from Anthropic is the crucial strategy for accurate RAG
- **[0:01:51]** The core problem: basic RAG frequently fails to retrieve the right chunks
- **[0:02:58]** Anthropic's evaluation chart: 9.9% failure with plain RAG, under 3% with all strategies combined
- **[0:05:29]** Contextual retrieval diagram: prompt each chunk with the full document to generate situating context
- **[0:07:00]** Triple-dash separator between prepended context and chunk content shown in the Neon DB
- **[0:08:03]** Building the pipeline in n8n: just one extra LLM call over basic RAG
- **[0:10:22]** Custom JavaScript chunker: split every 400 characters, no overlap
- **[0:12:19]** Cost management via prompt caching for the repeated full-document tokens
- **[0:13:23]** Use a small cheap model (GPT-4.1 Nano) for the context generation
- **[0:16:27]** Why Neon: serverless Postgres, autoscaling, branching, MCP server
- **[0:20:04]** Same embedding model must be used in pipeline and agent
- **[0:20:34]** Live test: agent answers the neural adaptation engine question using a retrieved contextual chunk
- **[0:23:10]** Same technique implemented in Python in the Crawl4AI RAG MCP server, to fold into Archon
- **[0:25:36]** Teases stacking contextual retrieval with query expansion and agentic RAG (Dynamous workshop)

## Transcript

[Raw transcript](../raw/the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide.md)
