Different Types of Chunking Methods
- Metric Coders
- Mar 29
- 2 min read
In this post, we’ll break down the most common types of chunking methods, and when to use each.
1. Fixed-Size Chunking
This is the most straightforward method: split data into chunks of equal size.
📌 Example:
Reading 1,000 rows at a time from a CSV file.
Splitting text into blocks of 500 characters or 100 tokens.
✅ Pros:
Easy to implement
Predictable performance
Works well with systems that require uniform input sizes (e.g., ML models)
❌ Cons:
Can break semantic meaning (e.g., splitting sentences in the middle)
May not align with natural boundaries in data
2. Content-Aware Chunking
Instead of using a fixed size, this method uses logical or semantic boundaries to split data—like sentences, paragraphs, or objects.
📌 Example:
Splitting text by sentence or paragraph
Breaking logs by timestamp or event ID
Parsing XML/JSON objects
✅ Pros:
Maintains context and meaning
Ideal for NLP and structured data tasks
❌ Cons:
Requires parsing or natural language understanding
Chunk sizes can vary wildly
3. Sliding Window Chunking
This technique involves creating overlapping chunks using a sliding window across the data.
📌 Example:
A window of 100 tokens with a stride of 50, creating overlapping text chunks.
✅ Pros:
Preserves context between chunks
Helps reduce loss of information at chunk boundaries
Useful in transformers and sequence models
❌ Cons:
Increases data volume due to overlap
More computation needed
4. Dynamic Chunking
Chunk size is not fixed—it adapts based on system resources or content characteristics (e.g., token count, punctuation density, image complexity).
📌 Example:
Splitting text by sentence until a token limit is reached
Adjusting chunk size based on available memory
✅ Pros:
Efficient resource usage
Balances semantic structure and size constraints
❌ Cons:
Harder to implement
May require real-time system feedback
5. Delimiter-Based Chunking
This method splits data using a specific delimiter—like newline characters, punctuation marks, or file separators.
📌 Example:
Splitting a transcript by timestamps
Chunking code by function or class definitions
Separating paragraphs using \n\n
✅ Pros:
Easy for structured or semi-structured data
Maintains logical boundaries
❌ Cons:
Depends on consistent delimiter presence
May not provide even-sized chunks
6. Byte or Token-Based Chunking
Frequently used in low-level systems or language models, this method breaks content by a certain number of bytes (in binary data) or tokens (in NLP).
📌 Example:
Tokenizing a prompt for GPT-4 and splitting it into 2048-token chunks
Processing 64KB of a file at a time
✅ Pros:
Precise control over data size
Compatible with language models and token-limited APIs
❌ Cons:
Token count ≠ word count (in text)
May split content mid-meaning
When to Use What?
Chunking Method | Best Use Case |
Fixed-Size | Simple batch jobs, ML training input |
Content-Aware | NLP, summarization, parsing logs |
Sliding Window | Sequence models, preserving context |
Dynamic | Adaptive systems, resource-sensitive environments |
Delimiter-Based | Structured data, parsing code or logs |
Token/Byte-Based | NLP models, file streaming, low-level processing |
Final Thoughts
Choosing the right chunking method can have a huge impact on your system’s accuracy, speed, and resource efficiency.
While fixed-size chunking might be fine for quick-and-dirty jobs, content-aware or dynamic methods often deliver better results—especially when context matters.
