Mastering Chunking for Better Semantic Retrieval with Large Language Models

In the ever-evolving landscape of natural language processing (NLP) and artificial intelligence (AI), the way we segment text plays a pivotal role in the quality of the insights we extract. Chunking — the practice of splitting large collections of text into smaller, semantically meaningful units — is a foundational technique that enhances the performance of large language models (LLMs) and semantic retrieval systems. By carefully choosing the size and scope of these “chunks,” we can improve the relevance, accuracy, and usefulness of results generated by advanced AI models.

In this blog post, we will:

• Define chunking and explain its core importance.
• Explore how chunk size affects semantic retrieval.
• Discuss how chunking interacts with dense vector representations.
• Examine the trade-offs involved in choosing different chunk sizes.
• Highlight techniques to embed more context without increasing chunk size.
• Offer guidance for determining an optimal chunk size for your unique application.

What is Chunking?

Chunking is the process of dividing a large corpus of text data into smaller sections, each intended to capture a coherent portion of meaning. The fundamental goal is to isolate logical “units” of text — such as a paragraph or a collection of sentences around a single topic — so that each chunk is easier for language models to understand and retrieve.

When text is split into appropriately sized chunks, these segments can be fed into an LLM’s context window more efficiently. Because LLMs have limits on how much data they can process at once, chunking ensures that each piece of text is fully “visible” to the model without overwhelming its capacity. As a result, the model can better interpret, summarize, or extract relevant information from these smaller, more digestible portions of text.

The Significance of Chunking in Semantic Retrieval

Semantic retrieval involves using vector-based methods to represent, store, and search text based on its meaning rather than simple keyword matches. Chunking is crucial here because it directly influences the relevance of the retrieved information.

Key Reasons Why Chunking Matters:

1. Fitting Within Context Windows:
   LLMs often have a maximum number of tokens they can process at once. Splitting text into well-sized chunks ensures that each piece can be fully considered by the model without running into length limitations.
2. Relevance and Accuracy:
   Smaller, thematically consistent chunks are generally more relevant to user queries. They help the retrieval system pinpoint the most meaningful parts of a document, which can then be used to answer questions, summarize information, or support downstream tasks.
3. Reducing Noise and Hallucinations:
   Smaller, more focused chunks reduce the risk that irrelevant information will overshadow what’s important. When irrelevant or competing ideas are crammed into a single large chunk, the model can struggle to identify which parts are pertinent. This can lead to “hallucinations” or misinterpretations.

Understanding Chunk Representation and Retrieval

Before examining chunk sizes, it’s helpful to understand how chunks are mathematically represented and how queries interact with these representations.

Vector Embeddings:
Each text chunk is converted into a dense vector representation (an embedding) that captures its semantic meaning in a high-dimensional space. These embeddings are then stored in a vector database, allowing for efficient similarity searches.

Rebuilding the Database for New Chunk Sizes:
Changing chunk sizes is not a quick fix; it often involves going back to the original text, re-embedding the newly formed chunks, and then re-indexing them. The entire retrieval pipeline — query embedding, similarity comparison, and ranking — depends on having accurate, consistent embeddings.

Query Interaction:
When a user submits a query, the system transforms that query into a vector and calculates similarity scores between the query vector and all the chunk embeddings. The top-ranked chunks are retrieved and then passed to the LLM. Chunk size affects these similarity computations. Smaller chunks might yield a more laser-focused semantic representation, while larger chunks might represent a broader context but risk blending multiple topics.

The Impact of Chunk Size on Retrieval Results

Small vs. Large Chunks:

• Small Chunks:
  Smaller chunks (e.g., 100–200 tokens) tend to offer higher precision. They often revolve around a single idea, making them ideal for queries that need pinpoint accuracy. If your query is very specific, small chunks are more likely to surface exactly what you need without extra noise.
• Large Chunks:
  Larger chunks (e.g., 300–400 tokens or more) contain more context. While this can be beneficial for queries that need broad understanding or when summarizing complex documents, it can dilute the semantic focus. If too many ideas coexist in one chunk, the retrieval system may return results that are less directly relevant to the query.

Example Scenario: Consider searching for the phrase “retrieval augmented generation.” With smaller chunks, the results might offer tightly focused passages directly related to that concept. Larger chunks might still retrieve relevant text, but the ranking and exact passages may shift slightly, incorporating more tangentially related details. While the overall relevant information could appear in both, the similarity scores and the order of retrieval might differ, subtly influencing the final answers provided by the LLM.

The Downside of Oversized Chunks

When chunks become too large, they may cover multiple topics. This complicates the retrieval process:

1. Dilution of Relevance:
   Instead of being laser-focused, the chunk’s representation in the vector space becomes muddled by multiple themes.
2. Burden on the LLM:
   The LLM must sift through more information within each chunk to find what is relevant. If the crucial content is buried deep in a large chunk, the model might miss it or fail to give it due importance.
3. Potential for Hallucination:
   With more data jammed into a single chunk, there’s a greater chance the model will latch onto misleading or irrelevant details, producing results that stray from the truth.

Embedding More Context Without Increasing Chunk Size

One clever workaround to add more context without inflating chunk size is often called the “small to big” approach. Instead of making the main chunk larger, you embed both the chunk and its neighboring sections of text. By doing this, you:

• Maintain a compact chunk that the model can handle easily.
• Add contextual embeddings from adjacent text, allowing the retrieval system to understand where the chunk fits in the broader document.

For example, if your chosen chunk size is 200 tokens, you might also consider the 200 tokens before and after it during the embedding process. This approach provides a richer semantic landscape without pushing the actual chunk size beyond what’s manageable or optimal.

Finding the Optimal Chunk Size

There is no universal “best” chunk size. The ideal size depends on factors like:

1. Nature of the Content:
   Are you dealing with lengthy research papers or shorter articles? Longer documents might benefit from larger chunks to ensure enough context is captured, while shorter or more varied text might require smaller chunks for higher precision.
2. The Embedding Model’s Strengths:
   Some embedding models excel at summarizing and capturing meaning in shorter segments, while others might handle longer passages well. Familiarity with your chosen model’s capabilities can guide chunk size decisions.
3. Expected Query Complexity:
   If you anticipate concise, targeted queries, smaller chunks can quickly surface relevant text. If queries are more open-ended or exploratory, larger chunks might provide richer, more nuanced answers.
4. Use Case Requirements:
   Consider how retrieved content is used. For question answering, smaller, more precise chunks may work best. For summarization or decision-making tasks, larger chunks that provide broader context may be more suitable.

It may also be beneficial to experiment with varying chunk sizes within the same database. Mixing different segment lengths can capture both granular details and broad context, potentially improving retrieval results across a wider range of query types.

Conclusion

Choosing the right chunk size is a balancing act. On one hand, smaller chunks offer precise, targeted retrieval and reduce the cognitive load on LLMs. On the other, larger chunks can encapsulate richer context but risk diluting relevance and increasing the model’s work in extracting key insights.

By considering the nature of your content, the strengths of your embedding model, the complexity of user queries, and the purpose of your retrieval system, you can determine the chunk size that best fits your needs. Experimentation and iterative refinement are key — try different configurations, measure the quality of results, and refine your approach.

In the realm of semantic retrieval, chunking is a powerful yet flexible tool. Mastering it will put you one step closer to building a system that efficiently and accurately surfaces the information your users need, ensuring that each retrieved result is both meaningful and actionable.

This standalone guide offers a comprehensive look at chunking, providing the insights needed to optimize retrieval strategies and improve the quality of large language model outputs. By understanding and applying these principles, you can confidently refine your chunking approach, ultimately delivering more relevant, accurate, and contextually rich results.