DEV Community

Cover image for A Beginner's Guide to Text Splitters in Langchain
AI Engineering profile image Damilola Oyedunmade
Damilola Oyedunmade for AI Engineering

Posted on • Edited on

A Beginner's Guide to Text Splitters in Langchain

#webdev #ai #beginners #programming

Large Language Models (LLMs) are powerful, but they come with a hard limit: context size. If you try to feed them a full book, an entire user manual, or even a long article, you’ll hit a wall fast. That’s where text splitters come in, and if you're building anything with LangChain, you can’t afford to ignore them.

Before we dive in, here’s something you’ll love:

Learn LangChain the clear, concise, and practical way.
Whether you’re just starting out or already building, Langcasts gives you guides, tips, hands-on walkthroughs, and in-depth classes to help you master every piece of the AI puzzle. No fluff, just actionable learning to get you building smarter and faster. Start your AI journey today at Langcasts.com.

Whether you're building an AI-powered search tool, a chatbot that answers questions from company docs, or an app that summarizes reports, you’re dealing with one unavoidable problem: how to break text down into chunks that LLMs can use, without losing meaning or flow.

This guide exists because too many developers run into bottlenecks here. They either dump full texts into prompts and get garbage output, or they chunk blindly and lose context. Both are avoidable mistakes.

LangChain’s text splitters offer a smart, flexible way to manage this challenge. Once you know how they work and when to tune them, your AI projects will become dramatically more efficient, accurate, and scalable.

Let’s break it down, step by step.

What Are Text Splitters?

Text splitters are tools that break large chunks of text into smaller, manageable pieces that an LLM can actually handle. But it's not just about cutting text to fit. It's about doing it in a way that keeps meaning intact and context preserved across each chunk.

LangChain makes this process easier by offering built-in text splitters that can intelligently segment documents based on characters, tokens, or formatting structure. These splitters aren't just slicing text randomly; they're designed to keep related ideas together, which is critical when you're asking a model to search, summarize, or answer questions accurately.

Think of it like prepping data for a conversation. You’re not dumping everything in at once. You’re handing the model clear, digestible bites that help it stay focused and coherent.

In the next section, we'll look at how this works in practice using LangChain’s core text splitter options.

How Text Splitters Work in LangChain

LangChain gives you a simple way to split text into chunks using customizable logic. The two main things you’ll set are:

  • Chunk size: how big each piece of text should be (usually measured in characters or tokens)
  • Chunk overlap: how much of the previous chunk should carry over to the next (helps preserve context across splits)

Here’s a basic example using one of the most common tools: RecursiveCharacterTextSplitter.

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 500,
chunkOverlap: 100,
});

const text = `Your long input string goes here...`;

const chunks = await splitter.splitText(text);

console.log(chunks);
Enter fullscreen mode Exit fullscreen mode

This tells LangChain:

  • Split long_text into 500-character chunks
  • Make each chunk share 100 characters with the one before it

That overlap is important. It helps prevent the model from losing track of important information that might get cut off between chunks.

LangChain also handles different kinds of separators intelligently. It tries to split at natural breakpoints first, like newlines, paragraphs, or sentences, before falling back to raw character cuts. This keeps the chunks more readable and coherent.

Next, we’ll break down the different types of text splitters LangChain offers and when to use each one.

Types of Text Splitters in LangChain

LangChain offers multiple splitter types, each designed for different kinds of input. Choosing the right one depends on your source text and how precise you need the chunking to be.

1. RecursiveCharacterTextSplitter (Most Common)

This is the default choice for general-purpose splitting.

  • How it works: Tries to split using a list of preferred separators (like \n\n, \n, space, etc.). If the text is still too large, it recursively tries the next separator.
  • Use case: Good for plain text, articles, blog posts, or any general content. 
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500,
  chunkOverlap: 100,
});
Enter fullscreen mode Exit fullscreen mode

2. CharacterTextSplitter

A simpler version that splits only by a single character, like a newline.

  • How it works: Splits text using just one separator.
  • Use case: Best when your text is cleanly structured with predictable breaks, like logs or CSV-like data. 
import { CharacterTextSplitter } from "langchain/text_splitter";

const splitter = new CharacterTextSplitter({
  separator: "\n",
  chunkSize: 300,
  chunkOverlap: 50,
});
Enter fullscreen mode Exit fullscreen mode

3. TokenTextSplitter (Advanced)

This one splits text based on token count, which is much closer to how LLMs actually read input.

  • How it works: Uses a tokenizer (like tiktoken) to count tokens instead of characters.
  • Use case: Ideal when you're hitting token limits or working with tight model constraints. 
import { TokenTextSplitter } from "langchain/text_splitter";

const splitter = new TokenTextSplitter({
  encodingName: "gpt2",
  chunkSize: 200,
  chunkOverlap: 20,
});
Enter fullscreen mode Exit fullscreen mode

4. MarkdownTextSplitter (Document-Aware)

Specifically built for markdown documents, this one splits by headers and sections.

  • Use case: Ideal for technical documentation, README files, and structured markdown content. 
import { MarkdownTextSplitter } from "langchain/text_splitter";

const splitter = new MarkdownTextSplitter();

const chunks = await splitter.splitText(markdownContent);
Enter fullscreen mode Exit fullscreen mode

Which One Should You Use?

Scenario Recommended Splitter
General-purpose text RecursiveCharacterTextSplitter
Line-separated logs or entries CharacterTextSplitter
Markdown files or docs MarkdownTextSplitter
Token-budget-sensitive workloads TokenTextSplitter

Coming up next, we’ll cover how to choose the right chunk size and overlap, and what happens when you get it wrong.

Choosing the Right Chunk Size and Overlap

Picking the right chunkSize and chunkOverlap can make or break how well your app performs with LLMs. Set them too large, and you risk hitting token limits or losing precision. Set them too small, and the model might miss the bigger picture.

Chunk Size

This controls how much text goes into each chunk. It’s usually measured in characters (or tokens, if you're using a token-based splitter).

  • Small chunk (e.g. 200–300 chars)

    Good for pinpoint tasks like sentence-level retrieval, but can lack context.

  • Medium chunk (e.g. 500–800 chars)

    Balanced — works well for most search, summarization, and Q&A tasks.

  • Large chunk (1000+ chars)

    More context per chunk, but can be inefficient if you're targeting precise answers.

Chunk Overlap

Overlap lets you repeat a portion of the previous chunk at the start of the next one. This helps the model stay “in the flow” when context carries over.

  • Example: With a chunkSize of 500 and an overlap of 100, your chunks might look like:

    Chunk 1: Characters 0–500
Chunk 2: Characters 400–900
Chunk 3: Characters 800–1300

This avoids cutting off mid-sentence or losing important transitions between ideas.

In Practice (JavaScript) 

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 600,
  chunkOverlap: 100,
});

const chunks = await splitter.splitText(text);

console.log(chunks.length);  // Check how many chunks were created
console.log(chunks[0]);      // Look at the first chunk’s content
Enter fullscreen mode Exit fullscreen mode

Tips for Picking Good Settings

  • Start with 500–800 characters per chunk
  • Use 50–100 characters of overlap
  • If you’re seeing broken thoughts or lost context, increase overlap
  • If you’re exceeding token limits, reduce chunk size

In the next section, we’ll walk through a real example of splitting a long document to see how these settings work in action.

Example: Splitting a Long Document

Let’s say you’re building a Q&A tool that needs to process a product manual. You don’t want to send the whole document to the LLM — you need to break it into smart chunks first.

Here’s how you can do that using LangChain JS:

Step 1: Load Your Text

For simplicity, we’ll use a long string here. In practice, you could load text from a PDF, web page, or file.

const manualText = `
Welcome to the SuperWidget 3000 user guide.

Getting Started:
1. Plug in the device.
2. Press the power button for 3 seconds.
3. Wait for the LED indicator to flash.

Troubleshooting:
If the device does not start, make sure the cable is firmly connected...
(Imagine many more paragraphs here)
`;
Enter fullscreen mode Exit fullscreen mode

Step 2: Apply the Text Splitter 

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500,
  chunkOverlap: 100,
});

const chunks = await splitter.splitText(manualText);
Enter fullscreen mode Exit fullscreen mode

Step 3: Review the Output 

console.log("Total Chunks:", chunks.length);
console.log("First Chunk:\n", chunks[0]);
Enter fullscreen mode Exit fullscreen mode

You’ll see the manual broken into overlapping, readable pieces, each one small enough for a model to understand, but with enough shared content to preserve context.

Why This Works

  • It respects natural breakpoints in the content
  • Each chunk stays under the token limit
  • Overlap prevents the model from “forgetting” what came before

This approach sets you up for accurate answers, better retrieval results, and smoother multi-turn interactions.

Next up: let’s look at common mistakes people make with text splitting and how to avoid them.

Text splitting isn't just a setup step, it's a core part of building anything serious with LLMs. Get it right, and your system becomes smarter, faster, and more reliable. Get it wrong, and you’ll hit limits, lose context, or confuse the model.

Quick Recap Checklist

✅ Choose a splitter that fits your content (character, token, markdown, or custom)

✅ Start with chunk sizes between 500–800 characters

✅ Add overlap (50–100 characters) to maintain flow

✅ Test your chunks before feeding them to the model

✅ Optimize based on your actual use case (search, summarization, Q&A, etc.)

So, here’s the exciting part: We are currently working on Langcasts.com, a resource crafted especially for all AI engineers, whether you're just getting started or already deep in the game. We'll be sharing guides, tips, and hands-on walkthroughs to help you master every piece of the puzzle.

We’ll update everyone here as new releases drop.

Want to stay in the loop? You can subscribe here to get updates directly.

Build with clarity. Build with confidence. Build seamlessly.

Top comments (0)