---
title: "13 RAG chunking strategies for better retrieval"
url: https://daily.dev/posts/13-rag-chunking-strategies-for-better-retrieval-0w1vk9pay
source_url: https://www.meilisearch.com/blog/rag-chunking-strategies
type: article
source: "Meilisearch"
published: 2026-08-05T08:22:21.461Z
updated: 2026-08-05T08:22:44.774Z
tags: ["llm", "rag", "vector-search"]
reading_time: 26
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# 13 RAG chunking strategies for better retrieval

**[Meilisearch](https://daily.dev/sources/meilisearch)** · 26 min read · 0 upvotes · 0 comments

## Summary

A comprehensive breakdown of 13 RAG chunking strategies, covering fixed-length, sentence-based, semantic, paragraph, sliding window, recursive, hierarchical, context-enriched, topic-based, modality-specific, agentic, subdocument, and hybrid approaches. Each strategy includes pros, cons, best-fit use cases, and Python implementation examples. Also covers how to choose chunk size, overlap guidelines (10–20%), evaluation methods using precision and recall, common mistakes like boundary problems and context loss, and best practices for balancing cost, accuracy, and complexity.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.meilisearch.com/blog/rag-chunking-strategies>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search)

[View this post on daily.dev](https://daily.dev/posts/13-rag-chunking-strategies-for-better-retrieval-0w1vk9pay)