---
title: "Chunking and Retrieval-Augmented Generation (RAG) | Margot.ai"
description: "Discover what Chunking is, how it works in RAG systems, the main techniques, how to choose chunk size and the role of Chunk Overlap in AI."
author: "user"
date: "2026-09-15T10:26:00+00:00"
language: "en-GB"
canonical_url: "https://www.margot-ai.com/en/chunking-rag-complete-guide"
source_url: "https://www.margot-ai.com/en/chunking-rag-complete-guide/"
content_type: "text/markdown"
---

# Chunking and Retrieval-Augmented Generation (RAG): Complete Guide


					![Chunking e Rag](https://www.margot-ai.com/wp-content/uploads/2026/09/Chuncking-e-rag.webp)







									**INDEX**

 	- [What Is Chunking and How Does It Work in RAG Systems](#ai)

 	- [How to Split Documents into Chunks](#perch%C3%A8)

 	- [Main Chunking Techniques for RAG Systems
](#tec)

 	- [How to Choose the Optimal Chunk Size
](#integrazione)

 	- [Chunk Overlap: What It Is and What It Is Used For](#over)

 	- [Margot AI Agent and RAG](#margot)


















## What Is Chunking and How Does It Work in RAG Systems







**Chunking** is a methodology used to make large amounts of information more manageable by **dividing extensive or complex documents into shorter text portions**, known as *chunks*, each characterized by its own **semantic coherence**. This process is particularly relevant in the context of **LLMs (Large Language Models)**, which operate within a *context window* of limited size and, consequently, cannot fully analyze very large files or entire archives in a single processing operation.

Within a [**Retrieval-Augmented Generation (RAG)**](https://www.margot-ai.com/en/retrieval-augmented-generation-ai-agents) architecture, each *chunk* is represented through a mathematical vector, known as an *embedding*, and subsequently stored in a **vector database**. When a request is submitted, the system identifies and retrieves the **most relevant content segments** in relation to the query. This allows the model to receive only the **useful context** required to formulate the answer, improving its accuracy and helping to reduce the risk of *hallucinations* or **AI hallucinations**.


















### How to Split Documents into Chunks







The creation of *chunks* generally begins with a preliminary **pre-processing** phase, which is required to clean the content and make it suitable for subsequent processing. During this phase, elements that do not provide informative value can be removed, such as **page numbers, repetitive headings and HTML tags**.

Once the text has been prepared, a **segmentation algorithm** is applied to divide it according to predefined criteria or based on syntactic and semantic characteristics. In more advanced processes, the generated *chunks* can also be enriched with **metadata**, such as titles, content summaries or source information. The resulting segments are then converted into *embeddings* and inserted into the **vector storage infrastructure**.


















### Main Chunking Techniques for RAG Systems







The way in which a document is divided depends on its structure, the nature of the information it contains and the type of search the system needs to perform. The main strategies include:

- **Fixed-Size Chunking:** divides the content into segments of the same length, using characters, words or tokens as a reference. It is computationally fast, but not very flexible and may interrupt sentences or concepts at the wrong point.

- **Recursive Chunking:** works through a hierarchy of natural separators, such as paragraphs, sentences and words, seeking to preserve the **syntactic structure and coherence of the content** during segmentation.

- **Semantic Chunking:** identifies changes in topic by analyzing the **semantic similarity between consecutive sentences** through *embeddings*. In this way, segment boundaries are mainly created when the meaning or topic being discussed changes.

- **Agentic and Dynamic AI Chunking:** relies on **AI agents or LLMs** to analyze the content and the user’s intent, allowing the system to dynamically identify the most appropriate splitting points and associate specific **metadata** with the *chunks*.

- **Hierarchical and Sliding Window Chunking:** preserves the relationships between parent and child elements within a document or uses **partially overlapping segments** to ensure greater continuity between consecutive blocks.


















### How to Choose the Optimal Chunk Size







Defining the correct **chunk size** requires finding a balance between the amount of information contained in each segment and the need to maintain a **sufficiently coherent context**. Excessively small *chunks* can separate information that should be interpreted together, resulting in incomplete or fragmented answers.

Conversely, overly large segments may contain a significant amount of irrelevant information, introducing greater **noise into the retrieval process**. This can reduce the accuracy of vector search and lead to an increase in **API-related costs**.

For **search and question answering (Q&A)** activities, blocks ranging from **200 to 500 tokens** are generally used, while for summarizing particularly complex content, the size can reach approximately **2,000 tokens**. However, there is no universal size: the most effective configuration should be identified through **practical testing and performance evaluations** based on the specific use case.


















### Chunk Overlap: What It Is and What It Is Used For







Con **Chunk Overlap** si identifica la parziale sovrapposizione di contenuto tra due *chunk* consecutivi. L’*overlap* viene generalmente impostato tra il **10% e il 20%**, mentre negli approcci basati su una finestra scorrevole può arrivare fino al **50%**.

La funzione dell’overlap è principalmente quella di **mantenere la continuità del contesto** tra segmenti adiacenti. In questo modo si riduce il rischio che **informazioni importanti**, collocate proprio in prossimità del punto di separazione, vengano suddivise tra due blocchi o vadano perse nelle fasi successive di vettorializzazione.

È comunque importante evitare valori di *overlap* eccessivamente elevati. Una sovrapposizione troppo ampia comporta infatti una maggiore **duplicazione delle informazioni**, con conseguente aumento dello spazio necessario per lo **storage nei database vettoriali** e dei tempi richiesti per l’elaborazione.


















### Margot AI Agent and RAG







**Margot AI** is a platform of **customizable AI agents** designed to support the automation of **Customer Service and business support**. The solution includes several automation-focused modules, including an **automatic ticket classifier**, a virtual assistant, a conversational chatbot and a tool for managing the **company Knowledge Base**.

Margot can integrate with corporate **Helpdesk, CRM and Ticketing** environments, enabling workflow automation, the management and summarization of complex conversations, and the routing of requests to the appropriate recipients.

Margot’s cognitive system, based on a **RAG architecture**, enables accurate responses by retrieving information directly from **company documents**. Data is processed in compliance with **privacy** requirements and **GDPR** compliance, without using user data to **train the models**.



















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