---
type: source
title: "Every RAG Strategy Explained in 13 Minutes (No Fluff)"
description: "There are ~11 practical RAG strategies spanning data preparation and retrieval, and the optimal system combines three to five of them for your specific use case."
youtube_id: tLMViADvSNE
url: https://www.youtube.com/watch?v=tLMViADvSNE
slug: every-rag-strategy-explained-in-13-minutes-no-fluff
published: 2025-11-02
duration: "0:12:52"
recency_rank: 57
raw: "../raw/every-rag-strategy-explained-in-13-minutes-no-fluff.md"
tags: [rag, retrieval, chunking]
created: 2026-07-21
updated: 2026-07-21
---

# Every RAG Strategy Explained in 13 Minutes (No Fluff)

Cole gives a dense, no-fluff rundown of the main RAG strategies, framing RAG in two phases: data preparation (chunk documents, embed them into a vector database or knowledge graph) and retrieval-augmented generation (embed the user query, search for similar chunks, feed them to the LLM as context). He stresses that no single strategy wins; the optimal solution usually stacks around three to five strategies tuned to the use case.

The retrieval-side strategies covered are reranking (two-step retrieval where a cross-encoder narrows a large candidate set to the few most relevant chunks so the LLM is not overwhelmed), agentic RAG (the agent chooses how to search, for example semantic search versus reading a whole document), knowledge graphs (a graph database of entity relationships built by an LLM, great for interconnected data but slow and expensive), contextual retrieval (the LLM prepends a description of how each chunk fits the document before embedding), query expansion (the LLM rewrites the query to be more precise), multi-query RAG (the LLM generates several query variants searched in parallel), hierarchical RAG (parent-child chunk relationships stored as metadata to search small but return big), self-reflective RAG (the LLM grades retrieved chunks and re-searches if the grade is low), and fine-tuned embeddings (domain-specific embedding models for 5-10% accuracy gains, including sentiment-based similarity).

On the data-preparation side he covers context-aware chunking (using an embedding model to find natural document boundaries, for example hybrid chunking via Docling) and late chunking (embedding the full document first, then chunking the token embeddings to preserve whole-document context). He closes with a tactical recommendation: if you only pick three to start, use reranking, agentic RAG, and context-aware chunking (specifically hybrid chunking with Docling), all demoed on Postgres with pgvector via Neon.

## Concepts covered

- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)
- [Stacking Context-Engineering Strategies](../concepts/strategy-stacking.md)
- [Chunking](../concepts/chunking.md)
- [Reranking](../concepts/reranking.md)
- [Agentic RAG](../concepts/agentic-rag.md)
- [Knowledge Graphs](../concepts/knowledge-graphs.md)
- [Contextual Retrieval](../concepts/contextual-retrieval.md)
- [Query Expansion](../concepts/query-expansion.md)
- [Multi-Query RAG](../concepts/multi-query-rag.md)
- [Context-Aware Chunking](../concepts/context-aware-chunking.md)
- [Late Chunking](../concepts/late-chunking.md)
- [Hierarchical RAG](../concepts/hierarchical-rag.md)
- [Self-Reflective RAG](../concepts/self-reflective-rag.md)
- [Fine-Tuned Embeddings](../concepts/fine-tuned-embeddings.md)
- [Vector Embeddings](../concepts/vector-embeddings.md)
- [Semantic Search](../concepts/semantic-search.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [Neon](../entities/tools/neon.md)
- [Postgres](../entities/tools/postgres-pgvector.md)
- [Graphiti](../entities/tools/graphiti.md)
- [Docling](../entities/tools/docling.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [GitHub](../entities/tools/github.md)

## Key moments

- **[0:00:54]** Core thesis: the optimal RAG solution combines around three to five strategies
- **[0:01:22]** RAG framed as two phases: data preparation (chunk + embed) and retrieval-augmented generation
- **[0:02:43]** Strategy 1, reranking with a cross-encoder for two-step retrieval
- **[0:03:32]** Strategy 2, agentic RAG, demoed live in Neon with chunk + document tables
- **[0:04:48]** Strategy 3, knowledge graphs via Graphiti for interconnected data
- **[0:05:37]** Strategy 4, contextual retrieval (Anthropic research), shown in Neon
- **[0:06:28]** Strategies 5 & 6, query expansion and multi-query RAG
- **[0:07:20]** Strategy 7, context-aware / hybrid chunking with Docling
- **[0:08:09]** Strategy 8, late chunking (embed before chunking)
- **[0:08:59]** Strategy 9, hierarchical RAG (parent-child chunks, search small return big)
- **[0:10:14]** Strategy 10, self-reflective RAG (grade chunks, re-search if low)
- **[0:10:38]** Strategy 11, fine-tuned embeddings, incl. sentiment-based similarity example
- **[0:12:24]** Golden nugget: start with reranking, agentic RAG, and context-aware chunking

## Transcript

[Raw transcript](../raw/every-rag-strategy-explained-in-13-minutes-no-fluff.md)
