You have chosen a good language model, a well-crafted prompt and a powerful vector database. However, your RAG system (Retrieval-Augmented Generation) is not achieving the accuracy you expect. Most likely, the problem is hidden not in the model or the retrieval algorithm, but in an overlooked step: chunking, that is, the document shredding strategy.

Chunking is the process of breaking large documents, transcripts, or technical documents into smaller and meaningful units that artificial intelligence systems can process. These pieces are then transformed into vector representations (embedding) and matched to a query. Incorrect fragmentation presents pieces of information that are disconnected from their context to the model, which directly reduces accuracy.

Table of Contents

• Why is chunking such a critical step?
• Where Does Chunking Come Into Action in the RAG Pipeline?
• How does Fixed-Size Chunking Work?
• Why is Recursive Chunking the Preferred Starting Point?
• When Does Semantic Chunking Really Make a Difference?
• What Are Document Structure-Based Fragmentation Strategies?
• What Does the 2026 Benchmark Data Say?
• How to Choose the Right Chunking Strategy
• TL; DR
• consequence

Why is chunking such a critical step?

Short answer: Because how well the model will respond depends largely on how consistent and meaningful the piece presented to it is.

Major language models (LLM) cannot process infinitely long text. Each model has a context window boundary, and what you present within that boundary determines the quality of the model's output. In the absence of proper fragmentation, two critical errors occur: too small fragments lose context, while too large fragments dilute relevance.

Vectara's research, published in NAACL 2025, embodies this equation: Measurements on 25 different fragmentation configurations and 48 embedding models reveal that chunking configuration affects retrieval quality as much as or more than embedding model selection. This finding suggests that many developer teams should invest the most time in, not the component (model selection and embedding), but in the initial phase.

A bad shredding strategy has chain effects. Critical relationships between concepts are severed, the retrieval step brings irrelevant or missing parts, and the model works with this inadequate context, producing erroneous or misleading responses. The problem is not caused by the inadequacy of the big language model, but by the poor quality of the raw material offered to it.

Where Does Chunking Come Into Action in the RAG Pipeline?

Chunking takes place in the RAG pipeline immediately after the document preprocessing step and just before the embedding generation step.

The process proceeds as follows: Raw documents are received into the system and go through the pre-processing step. Then the chunking strategy comes into play and splits the document into parts according to defined rules. Each piece is independently converted into a vector representation, and these vectors are stored in a vector database. When the user queries, the query is also converted to a vector and the closest parts are determined. These pieces are presented as context to the model and form the model response.

Each step in this chain depends on the quality of the previous one. An error in the shredding step negatively affects the performance of all subsequent steps. So trying to improve retrieval quality by modifying the embedding model may not be effective without first optimizing the fragmentation strategy.

How does Fixed-Size Chunking Work?

Fixed-size chunking is the most basic approach that splits a document when a certain number of tokens or characters is reached.

Its implementation is fast and deterministic: each piece becomes the same size, and the calculation cost is minimal. But the main weakness of this approach is that it does not take into account the semantic structure of the document. Splitting can occur in the middle of a sentence or paragraph, causing the fragment on both sides to lose context.

To alleviate this problem, a chunk overlap mechanism is used. A certain amount of tokens is shared between neighboring pieces, so that the information at the end of one track is also found at the beginning of the next track. The initial parameters verified in practice are as follows: the range 256 to 512 tokens is considered the most efficient point for most uses, while the recommended overlap rate is between ten and twenty percent, that is, between 50 and 100 tokens for a 512 token chunk.

Although fixed-size fragmentation offers a reasonable starting point in documents whose structure is unpredictable or inconsistent, better alternatives exist for documents with structural consistency.

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Why is Recursive Chunking the Preferred Starting Point?

Recursive chunking is an approach that divides the document first, starting with large structural units, then descending into progressively smaller units. RecursiveCharacterTextSplitter on Langchain is the most common implementation of this strategy.

Paragraphs are separated first by double line endings. If the parts still exceed the target size, splitting is applied by the end of a single line. If necessary, it is lowered to the level of sentences or words. This hierarchical approach preserves the natural flow of the document as much as possible.

The benchmark-validated starting point for general RAG applications in production environments is the use of RecursiveCharacterTextSplitter between 400 and 512 tokens with ten to twenty percent overlap. The calculation cost is low, the execution speed is high, and it is compatible with most types of documents. For this reason, RAG stands out as the basic strategy to be benchmarked in the early stages of development.

When Does Semantic Chunking Really Make a Difference?

Semantic chunking divides a document not by number of characters or tokens, but by meaning changes.

The process works in several steps: First the text is divided into sentences. A vector representation is created for each sentence. The similarity between the vectors of the neighboring sentences is then measured; when the similarity drops markedly, that point is determined as a fragment boundary. Ultimately, each fragment becomes a unit that is semantically consistent in itself, encompassing a single idea or topic.

Semantic fragmentation represents the transition to meaning-based segmentation rather than rule-based splitting; it transforms each sentence into vector notation, analyzes the similarity between these vectors, and determines the points at which the subject changes as semantic boundaries.

But it is not always correct to prefer semantic fragmentation. While semantic chunking appears to be theoretically superior, Vectara's research shows that it doesn't always provide a measurable improvement in realistic datasets, and the computational cost is significantly higher. Semantic fragmentation, especially in applications with long-contextual documents, can produce very small fragments, resulting in insufficient context for the model.

The most appropriate use cases are: knowledge bases, technical documentation covering a variety of topics, and RAG systems specific to areas where the content is high in meaning.

What Are Document Structure-Based Fragmentation Strategies?

Not all documents are plain text. Fragmentation approaches that consider this structure work better in documents that contain structural elements such as Markdown, HTML, PDF, or code files.

Markdown-based fragmentation divides the document into natural partitions according to the title hierarchy (H1, H2, H3). This approach is highly effective for technical documentation and knowledge base contents, as each chapter carries a semantic coherence within itself.

If HTML based fragmentation <section>, <article>, <p> fragments the document according to tags such as. For RAG systems that process web content, this strategy provides a fragmentation that preserves the visual structure of the document.

Code-based fragmentation divides source code files by function or class boundaries. The fact that each part contains an entire function when a RAG system needs to query the code documentation or code base significantly improves retrieval quality.

Parent-document retrieval, on the other hand, is a more advanced approach: while small parts are used for retrieval, the parent document or a larger section is accessed to give the model more context. This strategy gives effective results in situations where both precise retrieval and adequate context balance are required.

What Does the 2026 Benchmark Data Say?

Various assessments published in early 2025 and 2026 contain findings that call into question some common preconceptions about chunking strategies.

Vectara's research, published in NAACL 2025, found that fixed-size fragmentation consistently outperforms semantic fragmentation in realistic document sets in tests on 25 chunking configurations and 48 embedding models.

In addition, a systematic analysis conducted in January 2026 identified a “context gap” point, indicating that response quality had declined markedly around 2,500 tokens.

The effect of metadata enrichment is also notable: Adding superdata to chunks increased question-and-answer accuracy from the 50-60 percent band to 72-75 percent without any changes to the retrieval architecture.

When these findings are evaluated together, a practical framework emerges: Test the combination of recursive fragmentation and metadata enrichment before assuming the cost of semantic fragmentation. The effect of the chunking configuration can be more decisive than the chosen embedding model.

How to Choose the Right Chunking Strategy

The right strategy depends on your usage scenario, document type, and latency budget.

If you're in the rapid prototyping and validation phase, recursive fragmentation is enough to get started on the 512 token target with ten to twenty percent overlap. Installation takes five minutes and is compatible with most document types.

In structural documents (technical documentation, markdown, HTML), prefer the structure-based strategy that uses the document's own hierarchy. This approach, which respects the natural parts of the document, often improves retrieval quality.

Assess semantic fragmentation in areas where semantic consistency is critical and if there is an adequate computational budget; but be sure to measure it with your own dataset. Theoretical superiority may not always be confirmed in practice.

Don't skip metadata enrichment in any strategy. Adding metadata to parts, such as the source document, section title, creation date, or domain name, allows you to filter during querying and significantly increases accuracy.

Finally, the most important rule is: measure in your own dataset, with your own query patterns. The embedding model does not guarantee specific performance that receives benchmark scores, and your choice of chunking strategy likewise requires empirical validation.

TL; DR

Chunking is the most overlooked step of RAG systems but is just as effective as embedding model selection. Recursive chunking is a benchmark validated practical starting point with 512 tokens and 10-20 percent overlap. Although semantic fragmentation seems theoretically superior, it does not always yield consistent gains in real data sets, and the computational cost is higher. Metadata enrichment, on the other hand, is an easily neglected acquisition that significantly improves accuracy without changing the architecture. The choice of strategy should first be based on measurement with your own data, then optimization.

consequence

RAG systems work with the cumulative effect of chain decisions. The chunking strategy as well as model selection, embedding quality and retrieval algorithm form a critical link in this chain. Data science teams mostly invest in visible components and leave the fragmentation step to default settings. Yet 2025-2026 studies have clearly revealed the weight of this step in determining retrieval quality.

A good chunking strategy isn't a magic formula; it's a conscious design decision that understands your document type, query patterns, and system priorities. Making this data-driven decision builds the performance of your RAG system on a more solid foundation than unforeseen improvements.

Want to evaluate the retrieval performance of your RAG system and optimize your chunking strategy? Set up a technical exploratory meeting with our team.

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Sources

DataCamp, “Chunking Strategies for AI and RAG Applications

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