> For the complete documentation index, see [llms.txt](https://docs.stackai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.stackai.com/getting-started/core-ai-concepts/chunking.md).

# Chunking

### **Chunking: Optimizing Data Retrieval in Stack AI Workflows**

Chunking is a key technique in AI-powered document processing. In **StackAI**, using the right chunking strategy can greatly enhance how effectively machine learning models understand and extract data from documents.

***

#### **What is Chunking in StackAI?**

**Chunking** = Breaking large documents into smaller, manageable parts.

* Used in StackAI’s **"Files"** and **"Documents"** nodes.
* Ensures input fits within AI model token limits.
* Can be configured via the gear icon in relevant nodes.

***

#### **Chunking Methods**

**1. Naïve Chunking (Fixed-Length)**

Splits text by character, word, or token count.

* Pros:
  * Fast and simple to implement
  * Predictable processing time
* Cons:
  * May break sentences or ideas
  * Can reduce AI comprehension

***

**2. Sentence-Based Chunking**

Splits text along natural sentence boundaries.

* Pros:
  * Preserves meaning and structure
  * Enhances AI understanding
* Cons:
  * More computationally intensive
  * Chunk sizes can vary

***

#### **Optimizing Chunk Configuration**

**Chunk Size**

* Choose based on your model's capabilities.
* Tradeoff:
  * **Larger chunks** = better context but risk hitting token limits.
  * **Smaller chunks** = faster, but may lose coherence.
* Recommended: **200–1,000 tokens**

**Chunk Overlap**

* Adds continuity between chunks.
* Suggested: **15–30% overlap**

***

#### **Best Practices for Stack AI Users**

* Use **sentence-based chunking** for documents with rich content.
* Tune **chunk size** to match your AI model's limits.
* Experiment with **overlap percentages** to preserve context.
* **Iteratively test** to ensure optimal results.

***

#### **Technical Tips**

* Configure chunking inside "Files" and "Documents" nodes.
* Continuously monitor model performance as you adjust settings.
* Align your chunking strategy with your specific ML model needs.

***

#### **Why It Matters**

Mastering chunking helps:

* Improve document comprehension for AI
* Boost data extraction accuracy
* Deliver better performance across document-based workflows in StackAI


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