{"slug": "rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy", "title": "RAG Chunking Explained: How to Choose the Right Chunk Size and Strategy", "summary": "A developer explains the importance of chunking in Retrieval-Augmented Generation (RAG) systems, comparing it to cutting a pizza into manageable pieces. The post details how chunk size affects retrieval accuracy, balancing context and precision, and illustrates common pitfalls such as chunks that are too large or too small.", "body_md": "Imagine I give you a **whole pizza** and say:\n\n“Eat it.”\n\nYou look at it and think, *Sure, I can eat it.*\n\nBut now imagine I give you the same pizza without cutting it.\n\nCan you eat it comfortably?\n\n**Not really.**\n\nYou would probably cut it into smaller pieces first.\n\nAnd that is exactly what we do with large documents before giving them to an AI system.\n\nA 200-page PDF, a 50,000-line documentation file, or a huge company knowledge base is useful to us as a complete document.\n\nBut for a **Retrieval-Augmented Generation (RAG)** system, giving the entire document to the retrieval system is usually not the best idea.\n\nSo we break it down.\n\n```\nDocument\n   ↓\nChunks\n   ↓\nEmbeddings\n   ↓\nVector Database\n   ↓\nRetrieval\n   ↓\nLLM\n   ↓\nAnswer\n```\n\nThat process of breaking a large document into smaller, meaningful pieces is called **Chunking**.\n\nAnd here is the interesting part:\n\nChunking is not simply “splitting text into 500 characters.”\n\nIt is a **retrieval design decision**.\n\nThe wrong chunk can make the right information difficult to retrieve.\n\nLet's say we have this document:\n\n```\nCompany Employee Handbook\n\nChapter 1: Leave Policy\nEmployees receive 20 paid leaves every year...\n\nChapter 2: Work From Home\nEmployees can work remotely two days per week...\n\nChapter 3: Insurance\nEmployees are eligible for health insurance...\n```\n\nNow a user asks:\n\n“How many days can I work from home?”\n\nWe don't need the entire employee handbook.\n\nWe need the small section containing the **Work From Home Policy**.\n\nSo ideally:\n\n```\n                 DOCUMENT\n                     │\n        ┌────────────┼────────────┐\n        ↓            ↓            ↓\n     Chunk 1      Chunk 2      Chunk 3\n        │            │            │\n   Leave Policy   WFH Policy   Insurance\n                     │\n                     ↓\n               Relevant Chunk\n```\n\nThe goal is simple:\n\nRetrieve the smallest useful piece of information without destroying its meaning.\n\nThere are two opposite mistakes.\n\nImagine:\n\n```\n┌──────────────────────────────┐\n│ Leave Policy                 │\n│ Work From Home               │\n│ Insurance                    │\n│ Salary                       │\n│ Performance Review           │\n│ Travel Policy                │\n│ ...                          │\n└──────────────────────────────┘\n```\n\nYou ask:\n\n“How many leaves do I get?”\n\nThe answer exists somewhere inside the chunk.\n\nBut so does a lot of unrelated information.\n\nThe retriever has brought the answer **plus noise**.\n\nNow imagine:\n\n```\nChunk 1:\nEmployees are eligible for\n\nChunk 2:\n20 days of paid leave\n\nChunk 3:\nevery financial year.\n```\n\nThe original document contained a complete thought.\n\nOur chunking destroyed it.\n\nThe information exists.\n\nBut the **meaning is fragmented**.\n\nThis gives us the first important principle:\n\nChunking is a balance between context and precision.\n\nToo large:\n\n```\nMore context\n    ↓\nMore noise\n```\n\nToo small:\n\n```\nMore precision\n    ↓\nLess context\n```\n\nThe goal is somewhere in between.\n\nA typical RAG pipeline looks like this:\n\n```\n                    ORIGINAL DOCUMENT\n                           │\n                           ▼\n                    ┌─────────────┐\n                    │   Chunking  │\n                    └──────┬──────┘\n                           │\n              ┌────────────┼────────────┐\n              ▼            ▼            ▼\n           Chunk 1      Chunk 2      Chunk 3\n              │            │            │\n              ▼            ▼            ▼\n          Embedding     Embedding    Embedding\n              │            │            │\n              └────────────┼────────────┘\n                           ▼\n                    Vector Database\n                           │\n                           │ User Query\n                           ▼\n                     Query Embedding\n                           │\n                           ▼\n                       Retrieval\n                           │\n                           ▼\n                    Relevant Chunks\n                           │\n                           ▼\n                          LLM\n                           │\n                           ▼\n                        Answer\n```\n\nThere is one important thing to notice:\n\nChunking decides what the embedding model actually gets to understand.\n\nIf you change the chunks, you change the embeddings.\n\nIf you change the embeddings, you can change retrieval.\n\nAnd if retrieval changes, the context given to the LLM changes.\n\nSo chunking is not just preprocessing.\n\n**It is part of your retrieval architecture.**\n\nThis is probably one of the most misunderstood parts of chunking.\n\nSomeone might ask:\n\n“My LLM supports 128K tokens. Should I create 8K-token chunks?”\n\n**Not necessarily.**\n\nYour LLM's context window is only one part of the equation.\n\nYour chunk size depends on several things:\n\n```\n                     CHUNK SIZE\n                         │\n        ┌────────────────┼────────────────┐\n        ↓                ↓                ↓\n Embedding Model     Retrieval          LLM\n  Input Limit       Requirements    Context Budget\n        │                │                │\n        └────────────────┼────────────────┘\n                         │\n                         ▼\n                  Practical Chunk Size\n```\n\nAnd there are two more factors that matter:\n\nSo the decision becomes:\n\n```\nDocument Structure\n       +\nEmbedding Model Limit\n       +\nRetrieval Requirements\n       +\nLLM Context Budget\n       +\nQuestion Type\n       ↓\nCandidate Chunk Sizes\n       ↓\nEvaluation\n       ↓\nFinal Chunk Size\n```\n\nThis is why there is no universal:\n\n```\nchunk_size = 500\n```\n\nnumber.\n\nDon't start with “What chunk size does everyone use?” Start with “What information am I trying to retrieve?”\n\nThere is no universally best chunking strategy.\n\nA legal contract, a Markdown documentation page, and a Python codebase don't contain information in the same structure.\n\nSo why would we split them in exactly the same way?\n\nLangChain provides different text splitters, and its documentation recommends `RecursiveCharacterTextSplitter`\n\nas a strong starting point for generic text.\n\nLet's group the important approaches.\n\nThe simplest approach:\n\n```\nEvery 1000 characters → new chunk\n```\n\nExample:\n\n``` python\nfrom langchain_text_splitters import CharacterTextSplitter\n\nsplitter = CharacterTextSplitter(\n    chunk_size=1000,\n    chunk_overlap=100\n)\n\nchunks = splitter.split_text(text)\n```\n\nThe advantage?\n\n**Simple and predictable.**\n\nThe problem?\n\nIt doesn't necessarily care about meaning.\n\nIt can split:\n\n```\nat the end of a paragraph\n```\n\nor:\n\n```\nin the middle of a sentence.\n```\n\nSo fixed-size splitting is useful for simple, predictable data, but it is rarely the only strategy worth considering.\n\nThis is usually the best starting point for general text.\n\n``` python\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nsplitter = RecursiveCharacterTextSplitter(\n    chunk_size=1000,\n    chunk_overlap=200\n)\n\nchunks = splitter.split_text(text)\n```\n\nConceptually, the splitter tries to preserve larger natural boundaries before breaking the text further:\n\n```\nParagraph\n    ↓\nNewline\n    ↓\nSpace\n    ↓\nCharacter\n```\n\nSo instead of immediately cutting through a sentence, it tries to preserve meaningful blocks first.\n\n```\nDocument\n   │\n   ├── Paragraph\n   │      │\n   │      └── fits? → keep together\n   │\n   ├── Paragraph\n   │      │\n   │      └── too large?\n   │              ↓\n   │          split further\n   │\n   └── ...\n```\n\nThat's why it is such a useful generic starting point.\n\nWhen you don't know where to start, start simple. Then measure.\n\nCharacters and tokens are not the same thing.\n\nFor example:\n\n```\n1000 characters ≠ 1000 tokens\n```\n\nThe exact relationship depends on the tokenizer, language, and text.\n\nIf your downstream system has strict token constraints, token-aware splitting becomes useful.\n\n``` python\nfrom langchain_text_splitters import TokenTextSplitter\n\nsplitter = TokenTextSplitter(\n    chunk_size=500,\n    chunk_overlap=50\n)\n\nchunks = splitter.split_text(text)\n```\n\nToken-based splitting becomes particularly useful when you need precise control over how much tokenized content enters the embedding or downstream pipeline.\n\nSometimes the document already tells us how it should be chunked.\n\nConsider Markdown:\n\n```\n# Authentication\n\n## Login\n\nInformation about login...\n\n## Password Reset\n\nInformation about resetting passwords...\n\n# Payments\n\n## Refunds\n\nInformation about refunds...\n```\n\nWhy destroy that structure?\n\nInstead, preserve it.\n\nThe same idea applies to:\n\nFor example, a chunk can retain:\n\n```\nDocument: API Documentation\nSection: Authentication\nSubsection: Password Reset\n\nContent:\nTo reset your password...\n```\n\nNow the chunk contains both:\n\n**information + context**\n\nThat's much more useful for retrieval.\n\nCode is not prose.\n\nConsider:\n\n``` python\nclass PaymentService:\n\n    def process_payment(self):\n        ...\n```\n\nRandomly splitting every 500 characters can easily separate:\n\n```\nclass\n   ↓\nmethod\n   ↓\nimplementation\n```\n\nwhich destroys useful relationships.\n\nFor code, you want to preserve programming structure where possible:\n\n```\nRepository\n    ↓\nClass\n    ↓\nMethod\n    ↓\nLogical Block\n```\n\nLangChain provides language-aware splitting for multiple programming languages.\n\nSo for code:\n\nRespect the syntax before respecting the character count.\n\nNow we move from:\n\n“Where should I split the characters?”\n\nto:\n\n“Where does the meaning change?”\n\nConsider:\n\n```\nThe company was founded in 1998.\nIt started with five employees.\n\nThe company launched its first product in 2001.\nRevenue crossed $10M in 2005.\n```\n\nThere is a natural semantic transition between these ideas.\n\nSemantic chunking attempts to identify those changes rather than blindly following fixed character boundaries.\n\nThis can be useful for highly topic-driven documents.\n\nBut there is a trade-off.\n\nIt can introduce:\n\nSo don't automatically assume:\n\nSemantic = Better\n\nSometimes:\n\nSimple + predictable + evaluated > sophisticated + unevaluated.\n\nNow we reach the famous:\n\n```\nchunk_size=1000\nchunk_overlap=200\n```\n\nBut what do these numbers actually mean?\n\nChunk size defines how much content goes into one chunk.\n\nFor example:\n\n```\nchunk_size = 1000\n```\n\nmeans the splitter attempts to create chunks around that size according to the unit it uses.\n\nThat unit could be:\n\nFor example, `RecursiveCharacterTextSplitter`\n\nmeasures characters by default.\n\nSuppose the document says:\n\n```\nMachine learning models require training data.\n\nThe quality of this data directly affects model performance.\n\nGood data leads to better generalization.\n```\n\nIf we split aggressively:\n\n```\nChunk 1:\nMachine learning models require training data.\n\nChunk 2:\nThe quality of this data directly affects model performance.\n```\n\nThe relationship between the two chunks can become weaker.\n\nOverlap creates a bridge.\n\n```\nChunk 1\n┌───────────────────────────────┐\n│ Machine learning models...    │\n│ Training data affects...      │\n└───────────────┬───────────────┘\n                │\n             overlap\n                │\n                ▼\n        ┌───────────────────────────────┐\nChunk 2 │ Training data affects...      │\n        │ Good data leads to...         │\n        └───────────────────────────────┘\n```\n\nSo:\n\nOverlap protects context at the boundary.\n\nBut more overlap is not automatically better.\n\nToo much overlap means:\n\n```\nMore overlap\n     ↓\nMore chunks\n     ↓\nMore embeddings\n     ↓\nMore storage\n     ↓\nMore duplicate retrieval\n     ↓\nMore context repetition\n     ↓\nHigher cost\n```\n\nUse overlap when it helps preserve meaning across boundaries.\n\nLet's build a simple example.\n\nInstall the splitter package:\n\n```\npip install -U langchain-text-splitters\n```\n\nThen:\n\n``` python\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\ntext = \"\"\"\nChunking is important for Retrieval Augmented Generation.\n\nLarge documents are difficult to retrieve efficiently.\n\nBy breaking documents into smaller meaningful pieces,\nwe can retrieve only the information required to answer\na user's question.\n\"\"\"\n\nsplitter = RecursiveCharacterTextSplitter(\n    chunk_size=500,\n    chunk_overlap=50\n)\n\nchunks = splitter.split_text(text)\n\nfor i, chunk in enumerate(chunks):\n    print(f\"Chunk {i}\")\n    print(chunk)\n    print(\"-\" * 50)\n```\n\nThe flow is:\n\n```\nOriginal Text\n      │\n      ▼\nRecursiveCharacterTextSplitter\n      │\n ┌────┼────┐\n ↓    ↓    ↓\nC1   C2   C3\n │    │    │\n └────┼────┘\n      ↓\n Embeddings\n```\n\nFor token-controlled pipelines:\n\n``` python\nfrom langchain_text_splitters import TokenTextSplitter\n\nsplitter = TokenTextSplitter(\n    chunk_size=500,\n    chunk_overlap=50\n)\n\nchunks = splitter.split_text(text)\n```\n\nAnd for structured documents, choose the splitter that understands that structure rather than flattening everything into plain text.\n\nHere's the question that actually matters:\n\nWhich chunking method should I use?\n\nDon't choose based on popularity.\n\nChoose based on your data.\n\n| Document Type | Good Starting Strategy |\n|---|---|\n| General text | Recursive |\n| Markdown | Header / structure-aware |\n| HTML | HTML-aware |\n| Source code | Language-aware |\n| JSON | Structure-aware |\n| Highly topic-driven text | Semantic |\n| Strict token constraints | Token-based |\n| Legal / structured documents | Structure-aware |\n| Tables | Preserve table structure |\n\nThink about it like this:\n\n```\n                  What is my data?\n                         │\n        ┌────────────────┼────────────────┐\n        ↓                ↓                ↓\n     Plain Text       Structured         Code\n        │                │                │\n        ↓                ↓                ↓\n    Recursive        Structure-        Language-\n                     aware              aware\n        │                │                │\n        └────────────────┼────────────────┘\n                         ↓\n                Check Embedding Model\n                         ↓\n                  Choose Candidates\n                         ↓\n                     Evaluate\n```\n\nAnd that last step is important.\n\n**Choosing a chunking strategy is a hypothesis.**\n\nEvaluation tells you whether the hypothesis was correct.\n\nThis is where many chunking tutorials stop.\n\nThey shouldn't.\n\nBecause the real question isn't:\n\n“Did my document split successfully?”\n\nIt is:\n\n“Did splitting improve retrieval?”\n\nA chunk can look perfectly reasonable to a human and still perform badly in a retrieval system.\n\nSo we need an evaluation dataset.\n\nSuppose we're building an employee-policy chatbot.\n\nCreate questions such as:\n\n```\nQ1: How many annual leaves does an employee get?\n\nQ2: How many days can employees work remotely?\n\nQ3: What is the maternity leave duration?\n\nQ4: What happens to unused leaves?\n\nQ5: What is the resignation notice period?\n```\n\nFor each question, know where the answer actually exists.\n\nFor example:\n\n```\nQuestion:\nHow many annual leaves does an employee get?\n\nExpected Source:\nEmployee Handbook\n→ Leave Policy\n→ Section 2.1\n```\n\nNow we have something measurable.\n\nSuppose the correct chunk is:\n\n```\nChunk 17\n```\n\nOur retriever returns:\n\n```\nTop 5:\n\nChunk 91\nChunk 43\nChunk 17   ← Correct\nChunk 52\nChunk 8\n```\n\nThe correct chunk appeared in the top five.\n\nThat's useful.\n\nNow we can measure different aspects of retrieval quality.\n\nRecall@K asks:\n\nDid the relevant chunk appear anywhere in the top K results?\n\nFor example:\n\n```\nTop 5:\n\nChunk 91\nChunk 43\nChunk 17 ← Relevant\nChunk 52\nChunk 8\n```\n\nThe relevant chunk is present.\n\nTherefore:\n\n```\nRecall@5 = 1\n```\n\nor **100% for this query**.\n\nAcross many queries:\n\n```\nRecall@5 =\n\nQueries where relevant information\nappeared in top 5\n───────────────────────────────────\nTotal queries\n```\n\nSo if:\n\n```\n90 / 100\n```\n\nqueries retrieved the correct information within the top 5:\n\n```\nRecall@5 = 90%\n```\n\nIn simple terms:\n\nRecall asks: “Did I find it?”\n\nPrecision@K asks a different question:\n\n“How many of the retrieved results were actually relevant?”\n\nSuppose:\n\n```\nTop 5:\n\nChunk 17 → Relevant\nChunk 21 → Relevant\nChunk 42 → Irrelevant\nChunk 63 → Irrelevant\nChunk 91 → Irrelevant\n```\n\nThen:\n\n```\nPrecision@5 = 2 / 5\n            = 40%\n```\n\nSo:\n\n```\nRecall → Did I find the answer?\n\nPrecision → How much irrelevant information did I retrieve?\n```\n\nThis distinction becomes especially important when large chunks contain multiple unrelated topics.\n\nNow imagine two systems.\n\n```\nTop 5:\n\n1. Correct ✓\n2. Wrong\n3. Wrong\n4. Wrong\n5. Wrong\nTop 5:\n\n1. Wrong\n2. Wrong\n3. Correct ✓\n4. Wrong\n5. Wrong\n```\n\nBoth systems found the correct answer within the top 5.\n\nSo their **Recall@5 is the same**.\n\nBut are they equally good?\n\nNot really.\n\nSystem A put the correct result first.\n\nThat's where **MRR — Mean Reciprocal Rank** becomes useful.\n\nFor one query:\n\n```\nMRR = 1 / rank of first relevant result\n```\n\nSo:\n\n```\nCorrect at rank 1\n→ 1 / 1\n→ 1.0\n\nCorrect at rank 2\n→ 1 / 2\n→ 0.5\n\nCorrect at rank 3\n→ 1 / 3\n→ 0.33\n\nCorrect at rank 5\n→ 1 / 5\n→ 0.20\n```\n\nFor multiple queries, we take the average of these reciprocal ranks.\n\nFor example:\n\n```\nQuery 1 → Correct at #1 → 1.00\nQuery 2 → Correct at #2 → 0.50\nQuery 3 → Correct at #3 → 0.33\n\nMRR = (1.00 + 0.50 + 0.33) / 3\n    ≈ 0.61\n```\n\nSo:\n\nRecall tells you whether you found the answer. MRR tells you how high you ranked the first correct answer.\n\nThis is particularly useful when the order of retrieved results matters.\n\nA RAG system has multiple layers:\n\n```\n             User Question\n                    │\n                    ▼\n                 Retrieval\n                    │\n                    ▼\n              Retrieved Chunks\n                    │\n                    ▼\n                   LLM\n                    │\n                    ▼\n                  Answer\n```\n\nTherefore, evaluate both **retrieval** and **final answers**.\n\n```\nDid we retrieve the right information?\n```\n\nUseful metrics:\n\n```\nDid the model actually use that information correctly?\n```\n\nUseful dimensions:\n\n**Answer Correctness**\n\nDid the model produce the correct answer?\n\n**Answer Relevance**\n\nDid the answer actually address the user's question?\n\n**Faithfulness / Groundedness**\n\nIs the answer supported by the retrieved context?\n\nThis distinction matters.\n\nGood retrieval does not automatically mean a good answer.\n\nAnd:\n\nA good-looking answer does not automatically mean good retrieval.\n\nLet's say we have:\n\n```\n1,000 documents\n200 evaluation questions\n```\n\nWe test three configurations.\n\n```\nRecursive\nchunk_size = 256\noverlap = 32\nRecursive\nchunk_size = 512\noverlap = 64\nRecursive\nchunk_size = 1024\noverlap = 128\n```\n\nNow measure:\n\n| Configuration | Recall@5 | Precision@5 | MRR | Answer Correctness |\n|---|---|---|---|---|\n| 256 / 32 | 82% | 76% | 0.71 | 84% |\n| 512 / 64 | 91% | 83% | 0.86 | 92% |\n| 1024 / 128 | 93% | 61% | 0.78 | 87% |\n\nWhich one wins?\n\nProbably:\n\n**512 / 64**\n\nWhy?\n\nThe 1024 configuration has slightly higher Recall@5.\n\nBut it retrieves significantly more irrelevant information and ranks the relevant information less effectively.\n\nSo the final answer quality also drops.\n\nThis gives us another important principle:\n\nThe largest chunk is not necessarily the best chunk.\n\nAnd:\n\nThe goal isn't to maximize one metric. It's to find the best trade-off for your application.\n\nLet's say our chunk is:\n\n```\nEmployees can work remotely two days per week.\n```\n\nThat's useful.\n\nBut this is better:\n\n```\nDocument: Employee Handbook\nSection: Work From Home\nDepartment: HR\nYear: 2026\n\nContent:\nEmployees can work remotely two days per week.\n```\n\nWhy?\n\nBecause the content tells us **what** the information says.\n\nMetadata tells us **where it came from**.\n\nMetadata can also enable filtering:\n\n```\ndepartment = HR\ndocument = Employee Handbook\nyear = 2026\n```\n\nThen semantic retrieval can happen over a much more relevant subset.\n\nSo:\n\nGood chunking tells you what the text says. Good metadata tells you where it belongs.\n\n```\nTutorial says 500\n        ↓\nI use 500\n```\n\nThat's not a strategy.\n\nTest multiple values against your actual data.\n\nYour LLM may support 128K tokens.\n\nYour embedding model has its own input constraints.\n\nAlways check:\n\n```\nEmbedding model\n      ↓\nInput/token limit\n      ↓\nTokenizer\n      ↓\nActual chunk size\n```\n\nA legal contract and a Python codebase are not the same thing.\n\nUse the structure already present in the data.\n\nMore overlap doesn't automatically mean more context.\n\nIt can mean:\n\n```\nMore duplication\n      ↓\nMore embeddings\n      ↓\nMore storage\n      ↓\nMore retrieval noise\n      ↓\nHigher cost\n```\n\nImagine:\n\n```\n| Product | Price | Discount |\n|---------|-------|----------|\n| A       | 100   | 10%      |\n| B       | 200   | 15%      |\n```\n\nIf you blindly split this structure, you can lose relationships between columns and values.\n\nFor structured data:\n\nPreserve structure before splitting aggressively.\n\nIf I were starting a new RAG system tomorrow, I wouldn't immediately build a complicated semantic chunking pipeline.\n\nI'd start simple.\n\n```\nPDF?\nMarkdown?\nHTML?\nCode?\nLegal documents?\nTables?\nDocument\n   ↓\nHeaders\n   ↓\nSections\n   ↓\nParagraphs\n   ↓\nSentences\n```\n\nDon't destroy structure that already exists.\n\nAsk:\n\n```\nWhat is the model's input limit?\n\nWhat tokenizer does it use?\n\nHow does it behave with longer inputs?\n```\n\nRemember:\n\nThe embedding model is part of the chunk-size decision.\n\nFor example:\n\n```\nPlain text\n    ↓\nRecursive\n\nMarkdown\n    ↓\nHeader-aware\n\nCode\n    ↓\nLanguage-aware\n\nHighly topic-driven\n    ↓\nSemantic\n```\n\nFor example:\n\n```\n256\n512\n768\n1024\n```\n\nwith sensible overlap values.\n\nDon't treat these as universal recommendations.\n\nThey are simply **candidate configurations to test**.\n\nCreate questions based on what your users actually ask.\n\n```\nQuestion\n   ↓\nExpected Source\n   ↓\nExpected Information\n```\n\nTrack:\n\n```\nRecall@K\nPrecision@K\nMRR\n```\n\nTrack:\n\n```\nAnswer Correctness\nAnswer Relevance\nFaithfulness / Groundedness\n```\n\nAnd where useful, also track:\n\n```\nLatency\nToken Usage\nEmbedding Cost\nStorage Cost\n```\n\nSuppose:\n\n```\nRecursive Chunking\n```\n\nperforms almost as well as:\n\n```\nSemantic Chunking\n```\n\nbut is cheaper, faster, and easier to maintain.\n\nThen:\n\n**Take the simpler solution.**\n\nThe best chunking strategy isn't the most complicated one. It's the one that performs well on your data.\n\nPut everything together:\n\n```\n                         START\n                           │\n                           ▼\n                    Understand Data\n                           │\n                           ▼\n                 Preserve Data Structure\n                           │\n                           ▼\n                 Check Embedding Model\n                           │\n                           ▼\n                Understand User Questions\n                           │\n                           ▼\n                  Choose Initial Strategy\n                           │\n                           ▼\n                  Choose Chunk Size Range\n                           │\n                           ▼\n                     Choose Overlap\n                           │\n                           ▼\n                 Build Evaluation Dataset\n                           │\n                           ▼\n                  Run Retrieval Tests\n                           │\n             ┌─────────────┼─────────────┐\n             ↓             ↓             ↓\n         Recall@K      Precision@K      MRR\n             │             │             │\n             └─────────────┼─────────────┘\n                           ↓\n                   Evaluate RAG Answers\n                           │\n                           ▼\n             Correctness / Relevance /\n                    Groundedness\n                           │\n                           ▼\n                    Compare Results\n                           │\n                           ▼\n                  Tune and Repeat\n                           │\n                           ▼\n                       Production\n```\n\nThe important thing is that **chunking doesn't end when the document is split**.\n\nIt ends when you know those chunks are helping your retrieval system.\n\nLet's go back to our pizza.\n\nYou don't eat a whole pizza in one bite.\n\nYou cut it.\n\nBut you also don't cut it into **1000 tiny pieces**.\n\nBecause then you have created another problem.\n\nChunking works the same way.\n\n```\nToo Large\n    ↓\nToo Much Noise\n\nToo Small\n    ↓\nLost Context\n\nJust Right\n    ↓\nBetter Retrieval\n    ↓\nBetter Context\n    ↓\nBetter Answers\n```\n\nAnd the \"just right\" size is not a universal number.\n\nIt depends on:\n\n**your document structure + your questions + your embedding model + your retrieval strategy + your LLM context budget.**\n\nSo don't ask:\n\n“What is the best chunk size?”\n\nAsk:\n\n“What is the best chunk size for my data, my embedding model, and my retrieval problem?”\n\nBecause ultimately:\n\nChunking isn't about making documents smaller.\n\nIt's about making knowledge retrievable.\n\nAnd that's the real job of chunking.\n\n**Cut the document enough to retrieve what matters — but not so much that you lose why it matters.**", "url": "https://wpnews.pro/news/rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy", "canonical_source": "https://dev.to/shahstavan/rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy-2hf2", "published_at": "2026-08-27 11:37:07+00:00", "updated_at": "2026-08-27 11:48:19.055597+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "developer-tools"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy", "markdown": "https://wpnews.pro/news/rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy.md", "text": "https://wpnews.pro/news/rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy.txt", "jsonld": "https://wpnews.pro/news/rag-chunking-explained-how-to-choose-the-right-chunk-size-and-strategy.jsonld"}}