Mastering Document Chunking Strategies for Retrieval-Augmented Generation (RAG)

Sahin Ahmed, Data Scientist
16 min readJan 22, 2025

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Why Document Chunking is the Secret Sauce of RAG

Chunking is more than splitting a document into parts — it’s about ensuring that every piece of text is optimized for retrieval and generation. Here’s why it plays such a pivotal role in Retrieval-Augmented Generation (RAG):

Overcoming Token Limitations
Large language models like GPT or Llama have token limits. Without chunking, documents may exceed these constraints, leading to truncated or incomplete processing. Chunking ensures every segment fits neatly within these boundaries, making information accessible and actionable.

Improved Retrieval Accuracy
Search within RAG systems relies on matching queries to relevant document chunks. Well-chunked documents enhance the relevance of retrieval results by keeping related information together, reducing noise, and increasing precision.

Preserving Context for Generation
A poorly chunked document may split sentences, paragraphs, or ideas, leading to a loss of context. Chunking intelligently keeps semantic units intact, enabling the language model to generate coherent and accurate responses.

Enhanced Processing Efficiency
Chunking reduces the computational load by breaking documents into manageable parts. Smaller, meaningful chunks allow retrieval and generation components to work faster and more efficiently, even when handling massive datasets.

Custom Fit for Diverse Document Types
Not all documents are created equal. A legal contract, a research paper, and a customer support transcript each demand different chunking approaches. Tailored chunking ensures that each document type is treated in a way that preserves its unique structure and meaning.

Bridging Gaps Between Retrieval and Generation
The bridge between retrieving relevant information and generating human-like responses lies in how effectively the document is chunked. Properly chunked data enables seamless transitions, minimizing gaps and overlaps in understanding.

In essence, chunking acts as the glue that holds together the retrieval and generation processes, ensuring that your RAG system delivers accurate, context-aware, and meaningful results every time. With the stakes this high, mastering chunking is non-negotiable!

The Building Blocks of Effective Chunking

Now that we understand why chunking is essential, let’s break down what makes a chunk effective in a RAG system. A well-constructed chunk balances size, context, and retrieval relevance to optimize performance. Here are the key factors:

Size Matters

  • Token Limits: Each chunk must fit within the token limit of the language model (e.g., GPT models typically have limits of 4,000–32,000 tokens). Oversized chunks risk truncation, while undersized chunks might waste valuable tokens.
  • Balanced Granularity: Striking the right balance is crucial — chunks should be detailed enough to contain meaningful context but not so large that they overwhelm the model.

Context Preservation

  • Avoid Breaking Ideas: Chunks should preserve semantic units like sentences, paragraphs, or topics to ensure coherence.
  • Overlap for Continuity: Using overlapping chunks or sliding windows helps retain context across boundaries, ensuring that no key information is lost.

Relevance for Retrieval

  • Semantic Cohesion: Each chunk should represent a self-contained idea or topic. This improves the retrieval engine’s ability to match queries to relevant content.
  • Keyword Optimization: Including critical terms and phrases in each chunk increases the likelihood of retrieval success.

Alignment with Document Structure

  • Document-Specific Adaptation: Whether it’s a legal brief, a research paper, or a customer transcript, the chunking strategy should adapt to the document’s inherent structure.
  • Preserve Formatting: Retain important features like headings, bullet points, or table captions to maintain readability and context.

Efficiency and Scalability

  • Automation-Friendly: The chunking process should be automatable for large-scale documents or datasets.
  • Consistent Results: Ensure that the chunking method produces predictable and uniform outputs for easier integration with RAG workflows.

By considering these factors, you set the foundation for a chunking strategy that not only complements your RAG system but enhances its retrieval and generation capabilities. Up next, we’ll dive into specific chunking strategies that make this possible.

Fixed-Size Chunking: The Simplest Approach

Overview
Fixed-size chunking involves dividing a document into uniform chunks based on a predetermined size, typically measured in characters, words, or tokens. For example, splitting an encyclopedia into chunks of 200 words ensures consistent segment sizes for processing and retrieval.

Advantages

  1. Simplicity: Easy to implement, requiring minimal computational resources or additional logic.
  2. Predictability: Produces chunks of uniform size, simplifying integration with RAG systems.
  3. Scalable: Works well for large datasets, as it avoids complex calculations or document-specific analysis.

Disadvantages

  1. Loss of Context: May split sentences, paragraphs, or semantic units, leading to broken ideas or incomplete context.
  2. Irrelevance Risk: Uniform chunks may contain unrelated information, making retrieval less accurate.
  3. Inefficiency: Some chunks may include filler or redundant information, wasting valuable token space.

Best Use Cases

  1. Structured and Uniform Content: Works well for encyclopedias, dictionaries, or manuals where content is already highly organized and self-contained.
  2. Preliminary Prototyping: Ideal for quick experiments or when the focus is on validating a RAG pipeline, not optimizing retrieval.
  3. Limited Resources: Suitable for scenarios where simplicity and speed are prioritized over semantic accuracy.

Example: Breaking an Encyclopedia into 200-Word Chunks
Consider an online encyclopedia with articles on various topics. Using fixed-size chunking:

  • Each chunk contains exactly 200 words.
  • Sections such as “Introduction” or “History” might be split into two or more chunks if they exceed the limit.
  • This ensures the retrieval system processes all content uniformly, though it might lose the continuity of a single section.
from typing import List
import re

def word_splitter(source_text: str) -> List[str]:
# Replace multiple whitespaces with a single space
source_text = re.sub(r'\s+', ' ', source_text)
# Split text by single whitespace
return re.split(r'\s', source_text)

def get_chunks_fixed_size(text: str, chunk_size: int) -> List[str]:
text_words = word_splitter(text)
chunks = []
for i in range(0, len(text_words), chunk_size):
chunk_words = text_words[i: i + chunk_size]
chunk = ' '.join(chunk_words)
chunks.append(chunk)
return chunks

def get_chunks_fixed_size_with_overlap(text: str, chunk_size: int, overlap_fraction: float) -> List[str]:
text_words = word_splitter(text)
overlap_int = int(chunk_size * overlap_fraction)
chunks = []
for i in range(0, len(text_words), chunk_size):
start_index = max(i - overlap_int, 0)
end_index = i + chunk_size
chunk_words = text_words[start_index: end_index]
chunk = ' '.join(chunk_words)
chunks.append(chunk)
return chunks

# Example usage
if __name__ == "__main__":
text = (
"Natural language processing (NLP) is a subfield of linguistics, computer science, "
"and artificial intelligence concerned with the interactions between computers and human language."
)

chunk_size = 10 # Number of words per chunk
overlap_fraction = 0.2 # 20% overlap

# Generate chunks with overlap
chunks = get_chunks_fixed_size_with_overlap(text, chunk_size, overlap_fraction)

# Display chunks
for i, chunk in enumerate(chunks, 1):
print(f"Chunk {i}:\n{chunk}\n")
Chunk 1:
Natural language processing (NLP) is a subfield of linguistics, computer

Chunk 2:
linguistics, computer science, and artificial intelligence concerned with the interactions between computers

Chunk 3:
between computers and human language.

Sentence-Based Chunking

Overview
Sentence-based chunking involves splitting a document at natural sentence boundaries and grouping a defined number of sentences into each chunk. This strategy ensures that chunks contain coherent ideas, preserving the semantic integrity of the text.

Advantages

  1. Preserves Semantic Flow: Sentences remain intact, preventing mid-sentence splits that can disrupt meaning.
  2. Improved Retrieval Relevance: Chunks are more likely to contain coherent ideas, making retrieval results more meaningful.
  3. Adaptable: Easily adjustable by changing the number of sentences per chunk to fit specific use cases.

Disadvantages

  1. Variable Chunk Sizes: Sentences vary in length, leading to chunks of uneven token counts. This can cause inefficiency if some chunks exceed the model’s token limit.
  2. Complex Implementation: Requires reliable sentence detection, which can be challenging for poorly formatted or unstructured text.

Best Use Cases

  1. Text with Clear Sentence Boundaries: Effective for documents like news articles, essays, or structured reports where sentence boundaries are well-defined.
  2. Semantic Retention Critical: Ideal for applications where maintaining the coherence of ideas is a priority, such as summarization or question answering.
  3. Moderately Structured Content: Works well for semi-structured text, such as transcripts or emails.

Python Implementation of Sentence-Based Chunking
Here’s a Python implementation:

from typing import List
import re
import nltk
nltk.download('punkt')
nltk.download('punkt_tab')
from nltk.tokenize import sent_tokenize

def sentence_chunker(text: str, sentences_per_chunk: int) -> List[str]:
# Tokenize text into sentences
sentences = sent_tokenize(text)
chunks = []

# Group sentences into chunks
for i in range(0, len(sentences), sentences_per_chunk):
chunk_sentences = sentences[i: i + sentences_per_chunk]
chunk = ' '.join(chunk_sentences)
chunks.append(chunk)

return chunks

# Example usage
if __name__ == "__main__":
text = (
"Machine learning is a subset of artificial intelligence. It focuses on building systems that learn from data and improve over time. "
"Applications include healthcare, finance, and robotics. Advanced techniques like neural networks are key to its success."
)

sentences_per_chunk = 1 # Number of sentences per chunk

# Generate sentence-based chunks
chunks = sentence_chunker(text, sentences_per_chunk)

# Display chunks
for i, chunk in enumerate(chunks, 1):
print(f"Chunk {i}:\n{chunk}\n")
Chunk 1:
Machine learning is a subset of artificial intelligence.

Chunk 2:
It focuses on building systems that learn from data and improve over time.

Chunk 3:
Applications include healthcare, finance, and robotics.

Chunk 4:
Advanced techniques like neural networks are key to its success.

Paragraph-Based Chunking

Overview
Paragraph-based chunking divides text into chunks based on paragraph boundaries. Each paragraph becomes a chunk, preserving the logical structure and flow of the original document. This strategy ensures that every chunk represents a complete thought or section of the content.

Advantages

  1. Preserves Logical Structure: Entire paragraphs are kept intact, maintaining the original flow and context.
  2. Easy Implementation: Paragraphs are usually well-defined in structured documents, making them easy to identify and chunk.
  3. Context Retention: Larger chunks provide better context, which is beneficial for downstream tasks like summarization or question answering.

Disadvantages

  1. Variable Chunk Sizes: Paragraph lengths can vary significantly, which might lead to inefficiencies if chunks are too large or too small.
  2. Token Limit Risks: Long paragraphs might exceed the token limits of some language models, requiring further adjustments.

Best Use Cases

  1. Well-Formatted Documents: Ideal for structured content like essays, reports, or articles where paragraphs are clearly defined.
  2. Deep Context Needed: Useful when larger context chunks are important for tasks like summarization or document understanding.
  3. Hierarchical Documents: Works well for documents with subheadings and sections that align naturally with paragraph boundaries.

Python Implementation of Paragraph-Based Chunking

from typing import List
import re

def paragraph_chunker(text: str) -> List[str]:
# Split text into paragraphs based on double newlines
paragraphs = re.split(r'\n{2,}', text.strip())
return [paragraph.strip() for paragraph in paragraphs if paragraph.strip()]

# Example usage
if __name__ == "__main__":
text = (
"Machine learning is a subset of artificial intelligence. It focuses on building systems that learn from data and improve over time.\n\n"
"Applications include healthcare, finance, and robotics. Advanced techniques like neural networks are key to its success."
)

# Generate paragraph-based chunks
chunks = paragraph_chunker(text)

# Display chunks
for i, chunk in enumerate(chunks, 1):
print(f"Chunk {i}:\n{chunk}\n")

Semantic-Based Chunking

Overview
Semantic-based chunking involves splitting a document into chunks based on the meaning or topic coherence rather than predefined sizes or structural elements. This strategy leverages natural language processing (NLP) techniques to group text into segments that represent distinct ideas, topics, or subtopics.

Advantages

  1. Preserves Semantic Integrity: Ensures that chunks represent cohesive ideas, making retrieval and generation more contextually accurate.
  2. Improved Retrieval Quality: Queries are more likely to match relevant chunks since each chunk focuses on a single topic.
  3. Flexible and Adaptive: Works well for unstructured or complex documents, dynamically adjusting to the content’s flow.

Disadvantages

  1. Complex Implementation: Requires advanced NLP techniques, such as topic modeling or embeddings, to determine semantic boundaries.
  2. Resource Intensive: Processing large datasets with semantic analysis can be computationally expensive.
  3. Inconsistent Chunk Sizes: Chunks may vary significantly in size, potentially causing inefficiencies in token usage.

Best Use Cases

  1. Complex and Unstructured Documents: Ideal for research papers, blogs, or documents where topics are not clearly separated.
  2. Topic-Based Retrieval: Useful when retrieval accuracy for specific topics or themes is critical.
  3. Large Knowledge Bases: Works well for organizing large, diverse datasets into semantically meaningful units.
from typing import List
from sentence_transformers import SentenceTransformer, util
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize

def semantic_chunker(text: str, similarity_threshold: float = 0.85) -> List[str]:
# Tokenize text into sentences
sentences = sent_tokenize(text)

# Load pre-trained embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Encode sentences into embeddings
embeddings = model.encode(sentences, convert_to_tensor=True)

chunks = []
current_chunk = [sentences[0]]

# Compare sentence similarity to decide chunk boundaries
for i in range(1, len(sentences)):
similarity = util.pytorch_cos_sim(embeddings[i - 1], embeddings[i]).item()
if similarity > similarity_threshold:
current_chunk.append(sentences[i])
else:
chunks.append(' '.join(current_chunk))
current_chunk = [sentences[i]]

# Add the last chunk
if current_chunk:
chunks.append(' '.join(current_chunk))

return chunks

# Example usage
if __name__ == "__main__":
text = (
"""In the world of Retrieval-Augmented Generation (RAG), precision is everything.
Imagine asking a cutting-edge AI to find insights in a vast
legal document or summarize a dense technical paper,
only to receive a response that feels disjointed or incomplete.
The culprit? Poor document chunking. Chunking—breaking text into smaller,
meaningful units—isn’t just a technicality; it’s the foundation of
effective retrieval and context-aware generation.
In this blog, we’ll dive into the art and science of document chunking,
exploring strategies that can transform your RAG systems from average to exceptional"""
)

# Generate semantic-based chunks
chunks = semantic_chunker(text, similarity_threshold=0.95)

# Display chunks
for i, chunk in enumerate(chunks, 1):
print(f"Chunk {i}:\n{chunk}\n")
Chunk 1:
In the world of Retrieval-Augmented Generation (RAG), precision is everything.

Chunk 2:
Imagine asking a cutting-edge AI to find insights in a vast
legal document or summarize a dense technical paper,
only to receive a response that feels disjointed or incomplete.

Chunk 3:
The culprit?

Chunk 4:
Poor document chunking.

Chunk 5:
Chunking—breaking text into smaller,
meaningful units—isn’t just a technicality; it’s the foundation of
effective retrieval and context-aware generation.

Chunk 6:
In this blog, we’ll dive into the art and science of document chunking,
exploring strategies that can transform your RAG systems from average to exceptional

Recursive Chunking

Overview
Recursive chunking applies a hierarchical approach to divide documents. It starts with large chunks, such as sections or chapters, and recursively splits these into smaller segments like paragraphs or sentences until they meet size constraints. This method balances preserving the document structure and adhering to model token limits.

Advantages

  1. Preserves Document Hierarchy: Maintains the logical flow of structured documents by starting with high-level divisions.
  2. Balances Context and Token Limits: Ensures chunks are not too large while retaining sufficient context.
  3. Flexible and Scalable: Works for a wide range of document types, from simple articles to complex technical manuals.

Disadvantages

  1. Implementation Complexity: Requires a clear understanding of the document’s structure and multiple layers of splitting.
  2. Uneven Chunk Sizes: Chunks may still vary in size, especially in poorly formatted documents.
  3. Resource Intensive: Multiple passes through the document increase processing time and resource requirements.

Best Use Cases

  1. Structured Documents: Ideal for textbooks, technical manuals, and research papers with clear headings and sections.
  2. Token-Limited Models: Useful when working with models that have strict token limits, ensuring no information is truncated.
  3. Content with Hierarchies: Effective for nested content, like reports with sections, subsections, and bullet points.
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document

def recursive_chunk_with_langchain(documents: List[Document], max_chunk_size: int, overlap: int = 0) -> List[Document]:
"""
Perform recursive chunking using LangChain's RecursiveCharacterTextSplitter.

Parameters:
documents (List[Document]): List of LangChain Document objects.
max_chunk_size (int): Maximum allowed size of each chunk in characters.
overlap (int): Number of overlapping characters between chunks for context preservation.

Returns:
List[Document]: A list of chunked Document objects.
"""
# Initialize the recursive text splitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=max_chunk_size,
chunk_overlap=overlap,
separators=["\n\n", "\n", ".", " "]
)

# Chunk each document recursively
chunked_documents = []
for doc in documents:
chunks = splitter.split_text(doc.page_content)
for chunk in chunks:
chunked_documents.append(Document(page_content=chunk, metadata=doc.metadata))

return chunked_documents

# Example Usage
if __name__ == "__main__":
# Sample input documents
raw_text = (
"Section 1: Introduction\n"
"Machine learning is a subset of artificial intelligence. It focuses on building systems that learn from data and improve over time. "
"Applications include healthcare, finance, and robotics.\n\n"
"Section 2: Techniques\n"
"Advanced techniques like neural networks and reinforcement learning are key to machine learning success. "
"These methods have revolutionized fields like robotics and healthcare."
)

# Create LangChain Document objects
documents = [Document(page_content=raw_text, metadata={"source": "Sample Document"})]

# Define chunking parameters
max_chunk_size = 100 # Max size of each chunk in characters
overlap = 50 # Overlap between chunks for context preservation

# Perform recursive chunking
chunked_docs = recursive_chunk_with_langchain(documents, max_chunk_size, overlap)

# Display results
for i, chunk in enumerate(chunked_docs, 1):
print(f"Chunk {i}:\n{chunk.page_content}\n")

Document-Specific Chunking

Overview
Document-specific chunking tailors the chunking process to the unique structure and content of a document. Instead of applying a one-size-fits-all strategy, this approach adapts to the document’s inherent characteristics, such as headings, tables, bullet points, or other structural elements. It ensures that meaningful sections of the document are kept intact, enhancing retrieval and generation accuracy in RAG systems.

Advantages

  1. Customizable: Adapts to the structure and content of different document types, such as legal contracts, financial reports, or academic papers.
  2. Preserves Semantic Context: Retains the logical flow and meaning of the document, especially for specialized or formatted text.
  3. Improved Retrieval Accuracy: Chunks align with natural divisions in the document, increasing the likelihood of relevant retrieval.

Disadvantages

  1. Complex Implementation: Requires a deep understanding of the document’s format and structure.
  2. Time-Consuming: Tailoring the chunking process for each document type can be resource-intensive.
  3. Dependency on Preprocessing: May need advanced preprocessing to parse and recognize document elements.

Best Use Cases

  1. Legal Documents: Contracts or agreements where clauses and sections must remain intact for accurate retrieval.
  2. Technical Manuals: Documents with hierarchies (chapters, sections, sub-sections).
  3. Reports and Financial Statements: Content with tables, charts, and structured data that needs precise chunking.
  4. Multimedia Content: Documents containing images, captions, or embedded code snippets.
  • Use NLP models to detect and tag document-specific elements (e.g., headings, tables).
  • Leverage document parsers like PyPDF2, Apache Tika, or Unstructured.io for extracting and chunking structured files (PDFs, DOCX).
  • Apply embedding-based techniques to segment documents where structure is ambiguous.

Overlapping and Sliding Windows: Enhancing Context Across Chunks

Concept: Overlapping Chunks to Preserve Context
In Retrieval-Augmented Generation (RAG) systems, dividing a document into discrete chunks can lead to loss of context at the boundaries, especially when critical information spans multiple chunks. Overlapping involves creating chunks with shared content between consecutive segments, ensuring continuity and preserving semantic flow.

A sliding window technique achieves this by moving a window of a fixed size over the text with a specified overlap. Each chunk overlaps partially with the previous one, effectively carrying forward the context across boundaries.

Agentic Chunking: Optimizing Text for Task-Specific AI Processing

What is Agentic Chunking?
Agentic chunking is a text segmentation approach tailored to the tasks or roles an AI agent needs to perform. Unlike traditional chunking methods that prioritize structure or semantic cohesion, this method breaks down content into smaller, actionable chunks aligned with specific goals — such as answering a query, providing a summary, or making a decision.

The essence of agentic chunking lies in its task-driven design, where each chunk is crafted to provide the AI with the most relevant and useful information for a particular action. This approach ensures that the AI focuses on the task at hand with minimal processing overhead.

How It Works

  1. Task Identification: Define the specific tasks the AI agent will perform, such as summarization, question answering, or classification.
  2. Actionable Chunking: Segment the content into smaller sections that directly serve these tasks. For example:
  • Query-Based Chunks: Include potential answers or supporting evidence for a likely question.
  • Summary Chunks: Identify and group key points for summarization.
  • Decision-Making Chunks: Highlight pros, cons, or critical insights relevant to the decision.

3. Role Assignment: Attach metadata or labels to each chunk, specifying its intended purpose or relevance to a task.

Why Use Agentic Chunking?

  1. Task Optimization: Each chunk is pre-optimized for a specific role, reducing the cognitive load on the AI and improving efficiency.
  2. Precision: Ensures the AI accesses only the most relevant information for its task, avoiding distractions or irrelevant data.
  3. Multi-Task Support: Facilitates handling multiple objectives (e.g., summarizing and answering questions) by separating and labeling content for each purpose.
  4. Enhanced Interpretability: Provides clear cues for the AI, making its responses more focused and contextually accurate.

Example Use Cases

  • Customer Support: Agentic chunking can organize a FAQ database into sections optimized for answering customer queries versus summarizing policies.
  • Legal Analysis: Breaking down contracts into chunks optimized for answering specific legal questions or extracting key clauses.
  • Educational Content: Chunking lessons or articles into sections tailored for teaching, testing, or reviewing concepts.
  • Decision Support Systems: Preparing content with explicit arguments for and against a decision, aiding AI in recommending actions.

Agentic chunking bridges the gap between content segmentation and task-specific AI needs. By designing chunks with purpose, it empowers AI systems to operate more efficiently and effectively across diverse applications.

from langchain.output_parsers.openai_tools import JsonOutputToolsParser
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain.chains import create_extraction_chain
from typing import Optional, List
from langchain.chains import create_extraction_chain_pydantic
from langchain_core.pydantic_v1 import BaseModel
from langchain import hub
obj = hub.pull("wfh/proposal-indexing")

# You can explore the prompt template behind this by running the following:
# obj.get_prompts()[0].messages[0].prompt.template

llm = ChatOpenAI(model="gpt-4o-mini")


# A Pydantic model to extract sentences from the passage
class Sentences(BaseModel):
sentences: List[str]

extraction_llm = llm.with_structured_output(Sentences)


# Create the sentence extraction chain
extraction_chain = obj | extraction_llm


# Test it out
sentences = extraction_chain.invoke(
"""
On July 20, 1969, astronaut Neil Armstrong walked on the moon .
He was leading the NASA's Apollo 11 mission.
Armstrong famously said, "That's one small step for man, one giant leap for mankind" as he stepped onto the lunar surface.
"""
)


#source: https://towardsdatascience.com/agentic-chunking-for-rags-091beccd94b1

Best Practices for Implementing Chunking in RAG Systems

Chunking is the foundation of efficient and effective Retrieval-Augmented Generation (RAG) systems. To ensure optimal performance, it’s essential to follow these best practices when designing and implementing a chunking strategy:

1. Analyze the Document Structure Before Choosing a Strategy
Not all documents are the same. Analyze the document type, content structure, and use case before selecting a chunking method:

  • Structured Documents: Use paragraph-based or recursive chunking for reports, manuals, or research papers with clear sections and headings.
  • Unstructured Documents: Leverage semantic or embedding-based chunking for conversational data, social media posts, or notes where structure is minimal.
  • Task-Specific Needs: Consider agentic chunking for documents where AI tasks require specific actionable chunks.

2. Optimize Chunk Size to Balance Retrieval and Processing Costs
Chunk size plays a crucial role in RAG performance:

  • Too Large: Large chunks can exceed token limits or retrieve irrelevant information, reducing retrieval precision.
  • Too Small: Small chunks might miss the context necessary for effective generation, leading to fragmented results.
  • Ideal Approach: Test different chunk sizes to find the sweet spot that fits within model token limits while preserving context and semantic flow. For many tasks, chunks between 100–300 words often work well.

3. Use Hybrid Approaches
Combining chunking strategies can yield better results than using a single method:

  • Semantic + Recursive: Start with recursive chunking to divide a document into sections and refine chunks further using semantic-based chunking to preserve meaning.
  • Paragraph + Sliding Window: Use paragraph-based chunking with overlapping content to retain context across boundaries.
  • Agentic + Embedding-Based: Create task-specific actionable chunks and validate their coherence using embedding similarity.

4. Test and Iterate
Evaluate and refine the chunking process by assessing its impact on RAG system performance:

  • Evaluate Retrieval Quality: Test how well the retrieval system matches queries to the most relevant chunks. Poor matches may indicate that chunking boundaries are misplaced.
  • Monitor Model Outputs: Examine generated responses for coherence and relevance. Adjust chunk sizes or overlap if responses lack context.
  • User Feedback: Incorporate end-user feedback to identify cases where chunking fails to deliver accurate results.
  • A/B Testing: Experiment with different chunking strategies on the same dataset to determine which approach delivers the best results.

By following these best practices, you can design a chunking strategy that optimally supports your RAG system, balancing efficiency, retrieval precision, and the quality of generated outputs. Effective chunking not only improves system performance but also enhances the overall user experience.

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Sahin Ahmed, Data Scientist
Sahin Ahmed, Data Scientist

Written by Sahin Ahmed, Data Scientist

Data Scientist | MSc Data science|Lifelong Learner | Making an Impact through Data Science | Machine Learning| Deep Learning |NLP| Statistical Modeling

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