Chunking
Splitting documents into retrievable pieces before embedding them for RAG.
/ quick answer
Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section). Splitting documents into retrievable pieces before embedding them for RAG.
/ frequently asked
What is Chunking?
Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section).
What is an example of Chunking?
A 40-page PDF split into ~800-token overlapping chunks by markdown heading typically outperforms naive 500-char splits on Q&A accuracy.
Why does Chunking matter?
Splitting documents into retrievable pieces before embedding them for RAG.
What should I learn after Chunking?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Contextual Compression
Contextual compression is a technique used to reduce the size of the input context for a Large Language Model (LLM) while retaining its most relevant information, typically by summarizing or filtering.
- →Reranking
A second-pass model that reorders retrieved chunks by true relevance to the query.
Related workflows
Turn this into a repeatable process.
- →RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →Podcast → Article + Clips + Newsletter Pipeline
One podcast episode becomes an SEO article, 5 short-form clips and a newsletter — all AI-assisted.
- →Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
- →AI Support Agent Stack
Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
Related prompts
Reusable prompts for this job.
- →RAG Answer With Strict Citations
Force the LLM to answer only from provided chunks and cite them by ID.
Comparisons & alternatives
Pick between the options.
- →RAG vs Long Context Windows
Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.