What is chunking and why do we chunk our data

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Table of Contents

• 1 Types of Chunking Fixed-Size vs Semantic

• 2 Use Cases of Chunking in LLMs

• 3 Implementation Techniques and Best Practices

1 Types of Chunking Fixed-Size vs Semantic

Fixed-Size Chunking: This method splits text into uniform blocks, typically by token count or sentence count (e.g. every 500 tokens, sometimes with overlap). It’s straightforward – no complex analysis needed – and ensures chunks are of predictable length (Is Semantic Chunking worth the computational cost?). However, fixed slicing can break up semantic units; related sentences might end up in different chunks, potentially scattering context. Overlaps (repeating a few tokens from the end of one chunk at the start of the next) are a common trick to mitigate this, ensuring that important context at boundaries isn’t lost, at the cost of some redundancy.

Semantic Chunking: This approach divides text based on meaning – grouping sentences or passages that belong together topically or narratively . Instead of equal lengths, the goal is to cut only where a natural topic shift occurs. Two popular techniques are (a) breakpoint-based splitting, which computes semantic similarity between consecutive sentences and cuts when the similarity falls below a threshold, and (b) clustering-based splitting, which groups semantically similar sentences (not necessarily adjacent in the original text) into chunks . **** Semantic chunking strategies: (b) Breakpoint-based chunking uses a distance threshold on sentence embeddings to decide cut points, ensuring each chunk is semantically coherent. (c) Clustering-based chunking groups semantically similar sentences (even if non-consecutive in the document) into topic-based chunks. Both aim to produce coherent, self-contained segments of text . By preserving whole ideas, semantic chunks avoid chopping a concept in half – each chunk ideally has a clear focus (e.g. a full paragraph about a single topic) . The trade-off is that determining these boundaries requires extra computation (embedding each sentence and measuring semantic gaps, or performing clustering) . Chunks can also end up variable in length; some may be very short or very long, which can be challenging when feeding into LLMs with fixed context sizes.

Comparing Advantages and Drawbacks: Fixed-size chunking is computationally efficient and simple, which makes it attractive for real-world systems. It doesn’t depend on any external model to find boundaries and thus scales easily (Is Semantic Chunking worth the computational cost?) . Fixed chunks also behave consistently; developers can tune a token length and trust that all chunks follow it, which simplifies memory management and batching. The downside is semantic fragmentation: important context might be split across two chunks, forcing an LLM to piece together information or risking that a single chunk lacks necessary context. For example, splitting a Wikipedia article into fixed sentence-length chunks can isolate pronouns or references from their antecedents – e.g. a chunk containing the sentence “It has a rich history.” is ambiguous if the previous chunk named the subject (“Berlin”) (Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models). Such gaps can degrade the model’s understanding or a retriever’s ability to correctly embed the chunk.

Semantic chunking, by contrast, aims to maximize coherence within each chunk. This often makes each chunk more meaningful on its own, which can improve retrieval accuracy and make the LLM’s job easier (each chunk is like a self-contained piece of knowledge). In practice, however, semantic chunking doesn’t always outperform the simpler fixed approach. A 2024 study by Vectara found that fixed-size chunking often performed as well as or even better than semantic chunking in realistic RAG scenarios, despite the latter’s theoretical appeal . The benefits of semantic splitting were inconsistent: they appeared in some contrived cases (e.g. when documents were artificially stitched from disparate topics) but largely vanished on normal text . Moreover, semantic methods carry costs and risks. They add significant overhead – computing embeddings for every sentence and clustering or thresholding adds latency to data ingestion. They can also err: for instance, a clustering algorithm might group unrelated sentences that happen to use similar words (semantic false friends), or a breakpoint method might produce tiny one-sentence chunks if it’s too sensitive . These issues can lead to mis-grouped or overly fragmented chunks. In Vectara’s analysis, error propagation was noted: a semantic clustering that isn’t carefully tuned could mix topics (if embeddings suggest two distant sentences are similar) and over-segmentation was observed when a breakpoint method made nearly every sentence its own chunk, losing important context in each small piece . By contrast, fixed chunking with a bit of overlap tends to avoid those pitfalls by brute-force: include everything in rough blocks and let the model’s attention or a retriever figure it out, relying on the model’s robustness to pick out relevant bits from a slightly noisy chunk.

In summary, fixed-size chunking is robust, scalable, and often a strong baseline due to its simplicity (Is Semantic Chunking worth the computational cost?). Semantic chunking holds promise for more intelligible chunks and potentially fewer irrelevant tokens per chunk, but its real-world gains in 2024/25 studies have been modest and scenario-dependent . Many experts advise not to assume semantic chunking is automatically superior – its “magic” often fails to materialize in practice – and to weigh its computational cost against potential benefits. A hybrid approach is also possible: for example, using fixed-length chunks but only cutting at natural paragraph boundaries (to avoid splitting sentences), or using semantic methods selectively on portions of text where coherence seems crucial.

2 Use Cases of Chunking in LLMs

Memory Efficiency and Long Contexts: Chunking is fundamentally a strategy to cope with limited context windows and memory. Transformer-based LLMs have quadratic time/memory complexity in input length, so extremely long inputs (tens of thousands of tokens) are impractical to process in one go. By chunking a long text, an LLM can handle it piecewise. One line of research extends model context length via chunk-wise attention mechanisms. For instance, Dual Chunk Attention (DCA) (ICLR 2024) allows a model to handle >100k tokens without retraining by dividing the sequence into smaller chunks and performing attention in a chunk-local and chunk-interactive way (Training-Free Long-Context Scaling of Large Language Models). DCA “decomposes the attention computation for long sequences into chunk-based modules,” with separate attention for tokens within the same chunk and mechanisms to handle attention across chunks . In effect, the model processes one chunk at a time (each chunk being shorter than the original context limit) while still sharing some information between chunks to preserve context continuity. This dramatically reduces memory usage per attention operation, making ultra-long contexts feasible. Chunking-based approaches like this show minimal perplexity degradation even when scaling to contexts 8× longer than the model was trained on . Even without fancy new algorithms, chunking a long document and feeding it through an LLM in sections (perhaps with some overlap and an appropriate prompting strategy to maintain continuity) is a common tactic to work within context limits. Essentially, chunking trades off global context for feasibility: the model only sees a segment at a time, but that allows us to handle inputs of arbitrary size by iterating over chunks or selectively choosing which chunks to feed in.

From a memory perspective, chunking ensures we don’t load an entire huge text into GPU memory at once. This is also important in retrieval-augmented generation: if one tried to stuff an entire knowledge base or long document into the prompt, it would explode the memory and likely confuse the model. Instead, relevant pieces are retrieved and fed in – those pieces are chunks. As the Chroma research team points out, even though modern LLMs support longer contexts, dumping whole documents or corpora into the context window is inefficient and can distract the model with irrelevant detail (Evaluating Chunking Strategies for Retrieval | Chroma Research). For any given query, usually only a small fraction of the text is actually relevant; chunking lets the system isolate that part. This means the model processes far fewer tokens, saving computation and memory. In summary, chunking improves memory efficiency by keeping the working context lean – whether via breaking input into smaller serial pieces or by using sparse attention patterns that treat chunks independently.

Faster Inference: Reducing input size through chunking can significantly speed up inference. Since transformer computation scales quadratically with sequence length, splitting a long input into, say, five chunks of 2,000 tokens each and processing them separately can be faster than one 10,000-token run (depending on the degree of parallelism and overhead). In retrieval settings, chunking yields speed-ups by focusing the model on a handful of relevant chunks rather than a massive text. Instead of reading a 20-page document to answer a query, a RAG system might retrieve two 100-word chunks that likely contain the answer – the LLM only has to process ~200 words, which is both faster and more likely to produce a focused answer. Chroma’s 2024 report stresses that ideally “the LLM would only need to process the relevant tokens for a given query”, rather than all tokens in a large context . By trimming the fat (irrelevant content), chunking ensures fewer computations are needed per inference.

Beyond just smaller inputs, chunking enables other inference optimizations. A recent technique called ChunkAttention (ICLR 2024 under review) shows how chunking can accelerate multi-query or batched inference with shared content. ChunkAttention identifies when multiple inference requests share the same prefix (e.g., several users asking different questions on the same document) and chunks the key-value cache for that shared prompt segment (HERE). By doing so, the identical prefix tokens are processed once and the results reused for all relevant queries, rather than recomputed for each. This method, which organizes the shared prefix in a tree and batches attention on those chunked KV caches, was shown to speed up self-attention by 1.6–3× for long sequences (1k–8k tokens) that have overlapping sections . In essence, chunking here helps exploit repetition to cut down compute. Another example is NVIDIA’s TensorRT-LLM with chunked prefill (2024), which feeds long prompts to the GPU in smaller batches to increase utilization and avoid memory peaks, thereby improving throughput. All these optimizations leverage the idea of breaking the input or the attention computation into chunks so that work can be shared or done more efficiently. The net result is faster inference, especially in scenarios with long or repeated inputs.

Retrieval-Augmented Generation (RAG): Perhaps the most prominent use of chunking in the LLM context is in RAG systems. RAG combines an LLM with a vector database or search module: external documents are chunked into passages which are embedded and stored; at query time the most relevant chunks are retrieved and fed into the LLM as additional context (Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models). Chunking is crucial here for several reasons. First, it improves retrieval granularity: if your knowledge base is split into fine-grained chunks, the retriever can fetch a very specific piece of information (say, the paragraph containing the answer to a question) rather than an entire document (Evaluating Chunking Strategies for Retrieval | Chroma Research). This typically yields more accurate and grounded answers, because the LLM sees directly the evidence it needs. If documents weren’t chunked, a retriever would have to return whole documents, which might not even fit in the prompt or would contain a lot of irrelevant text. By chunking, we ensure that each retrieved unit is reasonably sized and on-topic.

Second, chunking reduces hallucination and increases precision in generation. The model’s output can be grounded in the retrieved chunks, which serve as source material. If those chunks are well-chosen (relevant and not cluttered with extraneous info), the model is less likely to go off on tangents. However, there is a balance: chunks shouldn’t be so small that they lose context, nor so large that they include unrelated content. Research has shown that chunk size can affect RAG performance. If chunks are too big, the retriever might pull in a lot of noise along with the signal, and the model could get confused or led astray (HERE). If too small, important context might be split such that no single chunk contains enough information to fully answer the query, requiring the model to somehow integrate multiple chunks (which current LLMs do only if all those chunks are provided). The Mix-of-Granularity (MoG) paper (arXiv 2024) explicitly tackles this: it notes that “coarse-granularity retrieval yields more information but with lower precision, while fine-granularity retrieval offers higher precision at the cost of efficiency” (Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation). Their solution dynamically chooses chunk size per query – illustrating how important chunking is to RAG success. They found, for example, a specific factual question might be best answered by a very fine chunk (just the sentence containing the fact), whereas a broad question benefits from a broader chunk or even combining information from multiple chunks .

Furthermore, chunking enables the use of vector indexes and similarity search. Dense retrievers work by embedding text into vectors; shorter, self-contained chunks produce embeddings that capture a single idea well (Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models). If one tried to embed an entire long document as a single vector, that vector would be an averaging of many topics and could end up not particularly close to any specific query. By chunking into semantically coherent pieces, each embedding is more “focused,” and retrieval of relevant pieces becomes more accurate . In fact, one study introduced “late chunking” to address the embedding context loss issue: they encode a whole document with a long-context model without splitting, then split the model’s output into chunk vectors, to get the benefit of both global context and local embedding granularity . This highlights that even at the embedding stage, how you chunk can impact the end-to-end performance of a RAG system.

In summary, chunking is at the heart of RAG – it enables large knowledge bases to be harnessed by LLMs. It improves relevance (retrieving small focused texts), keeps the LLM’s input length manageable, and underpins the use of efficient vector search algorithms. Ongoing research (like ChunkRAG, MoG, and CFIC in 2024) continues to refine chunking: e.g., filtering out less useful chunks, dynamically sizing chunks, or even avoiding upfront chunking by letting the model learn to extract snippets from full documents (HERE). But the baseline remains: good chunking is one of the reasons today’s LLM-powered QA systems can scale to huge corpora.

Distributed Training: Chunking also proves useful in the training of LLMs, especially for long-context or large-batch training across multiple GPUs. When dealing with extremely long training sequences (or very large models), one can use sequence chunking to parallelize training. Instead of one GPU holding the whole sequence’s computations, the sequence can be divided into chunks that are distributed across GPUs (sometimes called intra-data parallelism or context parallelism) (ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs). This is analogous to pipeline parallelism but along the sequence length dimension rather than the layer dimension. For example, Yao et al. (2024) propose a Fully Pipelined Distributed Transformer (FPDT) that uses a dedicated “sequence chunk pipeline.” They report a 16× increase in trainable sequence length on the same hardware by splitting sequences: “with our sequence chunk pipeline design, we can now train an 8B LLM with a 2 million token sequence length on only 4 GPUs” ( Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer). This was achieved while maintaining healthy GPU utilization (over 55% memory flush), indicating the efficiency of this chunking approach . In practice, this means instead of needing, say, 64 GPUs with huge memory to train on very long sequences, one can use a handful of GPUs and pass segments of the sequence through them in a pipelined fashion. Each GPU handles a chunk of the sequence (e.g., tokens 1-5000 on GPU1, the next 5000-10000 tokens on GPU2, and so on in a streaming manner). Gradients are still passed end-to-end, but at any given time each device deals with a manageable slice of the context. This significantly reduces memory per device and leverages parallelism to keep throughput high.

Beyond long-context, even standard LLM training uses chunking: the training corpus is typically broken into chunks up to the model’s maximum context length (or a bit less) before feeding it in. This is why we talk about training on, say, “1024-token sequences” – those sequences are chunks cut from longer text (with some strategy to ensure they don’t always start mid-sentence). Chunking in training data helps form batches of uniform size and avoids wasting computation on padding. Some pipelines employ curriculum learning where they start training on shorter chunks and gradually increase chunk length to help the model learn stability on long sequences ( arXiv:2412.18860v1 [cs.CL] 25 Dec 2024) (though specific 2024 references for this practice may be sparse, it’s a known technique). In distributed training, chunking the data (data parallelism) combined with model or sequence chunking (model parallelism or pipeline) is standard for scaling to trillions of tokens and very long contexts . The bottom line: chunking strategies facilitate splitting the workload in training, either by dividing the data across machines or by breaking sequences so that multiple machines can work on different parts of the sequence simultaneously.

3 Implementation Techniques and Best Practices

Implementing chunking for LLMs involves choosing chunk sizes and methods appropriate to the task, as well as understanding the trade-offs. Selecting optimal chunk sizes is often an empirical question. Recent research suggests there is no one-size-fits-all chunk length – it depends on the content and the application. If chunks are too large, you risk including irrelevant information; too small, you might lose context or require combining many chunks to answer a single query (Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation). As discussed, coarse chunks give more coverage (you’re likely to have the answer somewhere in a big chunk) but lower precision (that chunk also has lots of unrelated text) . Fine chunks are precise (each chunk tightly topical) but may sacrifice completeness, requiring the system to retrieve multiple chunks and piece them together, which can be inefficient or even ineffective if, say, the needed info is split in two chunks and the model only sees one. The ideal chunk size also varies with the query or task: a broad question like “Explain the French Revolution” might benefit from larger, broader chunks (or multiple chunks covering different aspects), whereas a specific question like “What year did X happen?” is best served by a pinpoint chunk that contains that fact . Recognizing this, advanced implementations use adaptive chunking. The Mix-of-Granularity (MoG) framework (2024) trains a router that chooses among multiple chunk sizes on the fly, effectively learning whether to use fine or coarse chunks for a given query . This kind of dynamic strategy can yield both high coverage and pertinence by adjusting chunk granularity to the situation – an idea that is gaining traction in the latest RAG systems.

For most practitioners, a good starting point is a moderate chunk length that encapsulates a complete thought (say, a paragraph or section). Common heuristics are on the order of a few hundred tokens per chunk (e.g. 200–500 tokens). Chroma’s evaluation of chunking strategies (2024) found that a simple “Recursive Character Text Splitter” set to ~200 tokens (with no overlap) performed very well across various metrics, nearly on par with the best specialized method (Evaluating Chunking Strategies for Retrieval | Chroma Research). This implies that chunking at natural boundaries (like paragraph breaks), aiming for a few hundred tokens, is a solid rule of thumb. Overlap between chunks can help maintain context for retrieval – e.g., repeating the last sentence of one chunk at the start of the next – but it should be used sparingly. Too much overlap means you’re storing and processing a lot of duplicate text, which wastes resources and can confuse the retriever (because the same text appears in multiple chunks). Indeed, reducing chunk overlap has been shown to improve certain retrieval metrics since it avoids redundant information across chunks . A small overlap (a sentence or half-paragraph) is often enough to ensure continuity without significant downsides.

In deciding between fixed or semantic chunking methods for implementation, consider the domain and resource constraints. Fixed-size (with boundary adjustments to avoid cutting sentences) is easier to implement and highly robust – many production systems stick with it because of its predictability and speed (Is Semantic Chunking worth the computational cost?). Semantic chunking might be worth it if your documents contain clearly delineated topics or sections that vary greatly in length, or if users often ask very specific questions where minimizing any extra context in the chunk is crucial. However, as research in 2024 indicated, investing heavily in better retrievers or embeddings can yield more benefit than investing in complex chunking algorithms . Vectara’s study noted that when a strong embedding model (like a state-of-the-art sentence transformer) is used, the difference between chunking strategies became less significant – the LLM and retriever could compensate as long as the chunks were reasonable . This suggests a best practice: start simple, and only refine chunking if you identify a clear need (e.g., the model is missing information because it was cut off, or retrieval is pulling in lots of irrelevant text indicating chunks are too large/granular).

Trade-offs to keep in mind: Semantic chunking entails running an embedding model on every sentence or passage during ingestion, which can be expensive for large corpora. There’s also a maintenance aspect – if underlying content changes, semantic boundaries might shift unpredictably, whereas fixed-size chunks can be updated incrementally. On the other hand, semantic chunks can reduce the number of chunks needed to cover an answer (since one chunk might conveniently hold an entire answer explanation), which can simplify the prompt construction. When implementing, one could combine approaches: e.g., use a fixed maximum size but always cut at the nearest sentence end before that limit (a semi-semantic approach), or use semantic splitting but enforce an upper bound on chunk length to avoid giant chunks.

Real-world best practices: In training LLMs, ensure that chunk boundaries align with logical breaks in text (for example, do not mix two unrelated documents in one training sequence without a separator, as the model could erroneously learn to interpolate them). It’s common to insert special tokens like <|endoftext|> between documents when packing multiple short texts into one chunk for training. This signals the model of a context break. Maintaining data order vs. shuffling is another consideration: some training pipelines shuffle chunks globally (losing document order), while others preserve sequential chunks of a document in order – the choice can affect how the model learns long-range coherence. There isn’t a single consensus, but if long-context capabilities are desired, ensuring the model sees some consecutive chunks from the same document during training (with correct ordering) can help it learn to carry information across chunks. In inference time for tasks like summarization of a book or lengthy report, a hierarchical chunking approach is a best practice: first chunk the document, summarize or embed each chunk, and if needed, feed summaries into a second-stage model for an overall summary. This two-level chunking mirrors how a human would break down a large task and is more tractable than trying to do everything in one pass.

Finally, always evaluate and iterate on chunking strategy using your specific application data. As Smith & Troynikov (Chroma 2024) demonstrated, different chunking strategies (fixed, semantic, varying sizes, overlaps) can differ by up to 9% in retrieval recall on the same data (Evaluating Chunking Strategies for Retrieval | Chroma Research). That’s significant – so testing a few options is worthwhile. Key metrics to watch include retrieval recall/precision (are relevant chunks being retrieved?), as well as downstream task performance (e.g., answer accuracy in a QA task, or factuality of generated responses). If you notice the model often says “I don’t have enough information,” maybe chunks are too small. If it includes irrelevant facts or seems confused by context, chunks might be too large or too numerous. Use error analysis to inform adjustments in chunk size or method. The cutting-edge research of 2024–2025 underscores that optimal chunking is context-dependent (Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation) – striking the right balance may involve dynamic approaches or clever heuristics, but even with simple methods, careful tuning of chunk size and overlap to your data can yield great results. In practice, a well-chosen chunking strategy will improve an LLM system’s efficiency and output quality by ensuring the model is always working with manageable, meaningful pieces of information.

Sources: Recent works and findings from 2024–2025, including arXiv papers on RAG chunking strategies , long-context model training ( Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer), efficient inference techniques (HERE), and industry research blogs evaluating chunking methods (Is Semantic Chunking worth the computational cost?) , have informed these insights. These high-impact sources highlight the evolving best practices in chunking for large language models. Each use case – from memory management to distributed training – showcases how thoughtful chunking enables LLMs to scale and perform more effectively.