10 Chunking Strategies for RAG: A Practical Comparison

Your RAG pipeline is only as good as its chunks. Feed an LLM a chunk that cuts a sentence in half, and it generates half an answer. Feed it a chunk that mixes two unrelated topics, and it hallucinates connections that do not exist. NeuroLink ships 10 chunking strategies so you can match the splitting method to the content type – and every strategy is available through a single static registry with lazy initialization and content-type recommendations.

This tutorial walks through each strategy with TypeScript code, benchmarks from our internal evaluations across 500 documents, and a decision framework so you know exactly when to reach for each one.

Why Chunking Matters

The quality of your chunks directly determines the quality of your retrieval, which in turn determines the quality of your generated answers. This is the fundamental chain in any RAG system.

flowchart LR
    DOC["Raw Document"] --> CHUNK["Chunking Strategy"]
    CHUNK --> EMB["Embedding"]
    EMB --> VEC["Vector Store"]
    VEC --> RET["Retrieval"]
    RET --> GEN["LLM Generation"]

    CHUNK -. "poor chunks" .-> BAD["Low Recall<br/>Hallucinations<br/>Incomplete Answers"]
    CHUNK -. "good chunks" .-> GOOD["High Recall<br/>Faithful Answers<br/>Accurate Citations"]

Three failure modes dominate when chunking goes wrong:

1. Boundary violations – A chunk splits a sentence, paragraph, or code block in the middle. The embedding captures incomplete meaning, and retrieval returns irrelevant fragments.
2. Topic mixing – A chunk contains content from two unrelated sections. The embedding averages the two topics, making it a weak match for queries about either one.
3. Context loss – A chunk is too small to carry enough context for the LLM to generate a useful answer, or too large to fit within the top-K context budget.

The right chunking strategy eliminates these failure modes by aligning chunk boundaries with the natural structure of the content.

The ChunkerRegistry: Factory Pattern with Lazy Loading

NeuroLink’s ChunkerRegistry is a static class that manages all 10 strategies through a factory pattern. Each chunker is created via a factory function when requested, keeping the initial bundle small. The registry auto-initializes on first access, so you never need to call a setup method manually.

import { ChunkerRegistry } from '@juspay/neurolink';

// List all available strategies (auto-initializes on first call)
const strategies = ChunkerRegistry.getAvailableStrategies();
console.log(strategies);
// ['character', 'recursive', 'sentence', 'token', 'markdown',
//  'html', 'json', 'latex', 'semantic', 'semantic-markdown']

// Get a chunker by strategy name
const chunker = ChunkerRegistry.get('markdown');

// Get a recommended strategy for a content type
const recommended = ChunkerRegistry.getRecommendedStrategy('markdown');
// Returns: 'markdown'

// Get default configuration for a strategy
const defaults = ChunkerRegistry.getDefaultConfig('recursive');
// { maxSize: 1000, overlap: 200, separators: ['\n\n', '\n', '. ', ' ', ''] }

The registry follows NeuroLink’s standard static factory pattern, which provides consistent lifecycle management across all subsystems. This same pattern powers the provider registry, the reranker registry, and the embedding model registry.

classDiagram
    class ChunkerRegistry {
        -chunkers Map~ChunkingStrategy, Factory~$
        -initialized boolean$
        +initialize()$ void
        +register(strategy, factory)$ void
        +get(strategy)$ Chunker
        +getAvailableStrategies()$ ChunkingStrategy[]
        +has(strategy)$ boolean
        +getRecommendedStrategy(contentType)$ ChunkingStrategy
        +getDefaultConfig(strategy)$ Record
        +reset()$ void
    }

    ChunkerRegistry --> CharacterChunker
    ChunkerRegistry --> RecursiveChunker
    ChunkerRegistry --> SentenceChunker
    ChunkerRegistry --> TokenChunker
    ChunkerRegistry --> MarkdownChunker
    ChunkerRegistry --> HTMLChunker
    ChunkerRegistry --> JSONChunker
    ChunkerRegistry --> LaTeXChunker
    ChunkerRegistry --> SemanticChunker
    ChunkerRegistry --> SemanticMarkdownChunker

Strategy 1: Character Split

The simplest approach. Splits text at a fixed character count with optional overlap. No awareness of content structure.

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

const result = await neurolink.generate({
  input: { text: 'Summarize this plain text file' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./data/raw-logs.txt'],
    strategy: 'character',
    chunkSize: 1000,
    chunkOverlap: 100,
    topK: 5,
  },
});

Configuration options: maxSize, overlap, separator, keepSeparator, minSize

Aliases: char, fixed-size, fixed

Pros:

• Predictable chunk sizes – every chunk is exactly maxSize characters (except the last)
• Zero overhead – no parsing, no NLP, no external calls
• Language-agnostic – works identically on English, CJK, or binary-encoded text

Cons:

• Splits mid-sentence, mid-word, even mid-token
• Produces the lowest retrieval quality in benchmarks
• Overlap is character-based, which may duplicate partial words

Best use case: Unstructured text with no formatting markers, such as raw log files or data dumps where you need consistent chunk sizes and do not care about semantic boundaries.

Strategy 2: Recursive Character Split

The Swiss Army knife. It tries to split at \n\n (paragraphs) first, then \n (lines), then . (sentences), then " " (words), and finally individual characters. This preserves the most meaningful boundaries when possible while guaranteeing that no chunk exceeds maxSize.

const result = await neurolink.generate({
  input: { text: 'What are the key configuration options?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./docs/guide.md'],
    strategy: 'recursive',
    chunkSize: 1000,
    chunkOverlap: 200,
    topK: 5,
  },
});

// The recursive chunker uses these separators by default:
// ['\n\n', '\n', '. ', ' ', '']
// Each level is tried in order until chunks fit within maxSize

Configuration options: maxSize, overlap, separators, keepSeparators, isSeparatorRegex, minSize

Aliases: recursive-character, langchain-default

Pros:

• Preserves paragraph and sentence boundaries when possible
• Falls back gracefully through the separator hierarchy
• Compatible with LangChain’s default behavior (same alias: langchain-default)
• Best general-purpose strategy when you do not know the document format

Cons:

• Does not understand document structure (headers, sections)
• Can still split within code blocks or tables
• Overlap calculation is character-based, not semantic

Best use case: General text documents, mixed-format content, or any situation where you want reasonable boundary handling without format-specific configuration. This is the default recommendation for most workloads.

Strategy 3: Token-Based

Splits text by token count using model-specific tokenizer approximations. Essential when your embedding model has a strict token limit (like 512 tokens for many embedding models) and you need precise budget management.

const result = await neurolink.generate({
  input: { text: 'Explain the rate limiting configuration' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./docs/api-reference.md'],
    strategy: 'token',
    chunkSize: 512,
    topK: 5,
  },
});

// Configure tokenizer for your model family
// Supported: cl100k_base (GPT-4), p50k_base (Codex), r50k_base (GPT-3)

Configuration options: maxSize, overlap, tokenizer, maxTokens, tokenOverlap

Aliases: tok, tokenized

Pros:

• Respects model token limits precisely – no truncation surprises
• Each chunk uses the full embedding capacity of the model
• Includes estimatedTokens in chunk metadata for cost tracking

Cons:

• Uses approximate tokenization (word-based heuristic at ~4 chars/token for GPT models)
• Does not respect content structure
• Slightly more overhead than character splitting due to token estimation

Best use case: When your embedding model has a strict token limit and you need to maximize information density per chunk. Particularly useful with models like text-embedding-3-small (8191 token limit) where you want chunks sized at exactly 512 tokens.

Strategy 4: Sentence-Based

Splits at sentence boundaries (periods, exclamation marks, question marks) and groups sentences to fill the chunk size. Each chunk contains complete thoughts.

const result = await neurolink.generate({
  input: { text: 'What does the documentation say about authentication?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./docs/security-guide.md'],
    strategy: 'sentence',
    chunkSize: 1000,
    topK: 5,
  },
});

// Fine-tune sentence detection
// sentenceEnders: ['.', '!', '?'] (default)
// minSentences: 1 (minimum sentences per chunk)
// maxSentences: 10 (maximum sentences per chunk)

Configuration options: maxSize, overlap, sentenceEnders, minSentences, maxSentences

Aliases: sent, sentence-based

Pros:

• Every chunk contains complete sentences – no cut-off mid-thought
• Natural for Q&A applications where each chunk should answer a question
• Sentence overlap preserves context between chunks (overlap is measured in sentences, not characters)

Cons:

• Relies on punctuation for boundary detection, which fails on some content types (code, logs, tables)
• Does not understand paragraph or section boundaries
• Very long sentences (common in legal or academic text) may exceed maxSize

Best use case: Natural language prose, FAQ documents, Q&A knowledge bases, and any content where sentence integrity matters more than structural awareness.

Strategy 5: Markdown

Splits at heading boundaries (#, ##, ###) while preserving the heading hierarchy as metadata. Code blocks are protected from splitting by default.

const result = await neurolink.generate({
  input: { text: 'What are the main configuration sections?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./README.md'],
    strategy: 'markdown',
    chunkSize: 1500,
    topK: 5,
  },
});

// Markdown-specific options:
// headerLevels: [1, 2, 3] -- which heading levels trigger splits
// preserveCodeBlocks: true -- keep code blocks intact
// includeHeader: true -- prepend the section header to each chunk
// stripFormatting: false -- keep markdown formatting in chunks

Configuration options: maxSize, overlap, headerLevels, preserveCodeBlocks, includeHeader, stripFormatting

Aliases: md, markdown-header

Pros:

• Preserves document structure – each chunk corresponds to a logical section
• Header metadata enables faceted search (filter by section title)
• Code blocks stay intact, preventing syntax fragmentation
• The heading is prepended to each chunk, providing section context to the embedding

Cons:

• Only works with markdown-formatted content
• Very long sections (no sub-headings) may still produce oversized chunks
• Does not understand semantic boundaries within a section

Best use case: Documentation, README files, technical guides, and any markdown-based knowledge base. This is the default recommendation for documentation workloads.

Strategy 6: HTML

Splits at semantic HTML tags (<article>, <section>, <p>, <div>) while preserving structural elements like tables, code blocks, and lists.

const result = await neurolink.generate({
  input: { text: 'Find the pricing details from this webpage' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./scraped/pricing-page.html'],
    strategy: 'html',
    chunkSize: 1000,
    topK: 5,
  },
});

// HTML-specific options:
// splitTags: ['div', 'p', 'section', 'article', ...] -- tags that trigger splits
// preserveTags: ['pre', 'code', 'table', 'ul', 'ol'] -- keep these intact
// extractTextOnly: false -- strip all HTML tags and return plain text
// includeTagMetadata: true -- store tag name and attributes in chunk metadata

Configuration options: maxSize, overlap, splitTags, preserveTags, extractTextOnly, includeTagMetadata

Aliases: html-tag, web

Pros:

• Understands DOM structure – splits at semantically meaningful boundaries
• Preserves tables, lists, and code blocks as atomic units
• Tag metadata enables filtering (only search within <article> chunks)
• extractTextOnly mode strips markup for cleaner embeddings

Cons:

• Requires valid HTML input (malformed HTML may produce unexpected splits)
• Does not handle JavaScript-rendered content (use a headless browser first)
• Large <div> blocks with no sub-structure may exceed maxSize

Best use case: Web scraping pipelines, email templates, HTML documentation, and any content where DOM structure carries semantic meaning.

Strategy 7: JSON

Splits at object boundaries while respecting nesting depth. Every chunk contains valid JSON, and the JSON path is included in metadata for traceability.

const result = await neurolink.generate({
  input: { text: 'What are the default configuration values?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./config/defaults.json'],
    strategy: 'json',
    chunkSize: 1000,
    topK: 5,
  },
});

// JSON-specific options:
// maxDepth: 10 -- maximum nesting depth to traverse
// splitKeys: [] -- specific keys that trigger splits
// preserveKeys: [] -- keep these keys together in one chunk
// includeJsonPath: true -- include JSON path in chunk metadata

Configuration options: maxSize, maxDepth, splitKeys, preserveKeys, includeJsonPath

Aliases: json-object, structured

Pros:

• Every chunk is valid JSON – no broken structures
• JSON path metadata enables precise source attribution
• Depth-aware splitting respects nested hierarchies
• Gracefully handles invalid JSON by falling back to plain text

Cons:

• Deeply nested objects may produce chunks that are too large or too small
• Array elements may be split across chunks even when logically related
• JSON formatting (pretty-print) increases chunk size

Best use case: API responses, configuration files, structured data exports, and any JSON-formatted content where structural integrity matters.

Strategy 8: LaTeX

Splits at \section, \subsection, \begin{environment}, and math block boundaries. Equations and proofs stay intact as atomic units.

const result = await neurolink.generate({
  input: { text: 'What is the main theorem in this paper?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./papers/attention-is-all-you-need.tex'],
    strategy: 'latex',
    chunkSize: 1500,
    topK: 5,
  },
});

// LaTeX-specific options:
// splitEnvironments: ['section', 'subsection', ...] -- section commands that trigger splits
// preserveMath: true -- keep equation/align/gather environments intact
// includePreamble: true -- include the document preamble as the first chunk

Configuration options: maxSize, overlap, splitEnvironments, preserveMath, includePreamble

Aliases: tex, latex-section

Pros:

• Math environments (equation, align, gather) are never split
• Section hierarchy is preserved in chunk metadata
• Preamble (packages, macros) can be included as a reference chunk
• Understands LaTeX-specific structure that other chunkers would destroy

Cons:

• Only works with LaTeX-formatted content
• Custom macros may not be recognized without preamble context
• Very long sections without subsections may exceed maxSize

Best use case: Academic papers, scientific documents, mathematical content, and any LaTeX-formatted material where equations and structured sections must stay intact.

Strategy 9: Semantic Chunking

Uses embedding similarity to detect where the topic changes within a document. Instead of splitting at structural markers, it splits where the meaning shifts. This is the highest-quality strategy but requires embedding API calls during chunking.

const result = await neurolink.generate({
  input: { text: 'What are the performance implications of caching?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./docs/architecture-guide.md'],
    strategy: 'semantic',
    chunkSize: 1000,
    topK: 5,
  },
});

// Semantic-specific options:
// similarityThreshold: 0.7 -- cosine similarity below this triggers a split
// modelName: 'text-embedding-3-small' -- embedding model for similarity
// provider: 'openai' -- embedding provider
// joinThreshold: 100 -- minimum segment size before semantic analysis

Configuration options: maxSize, overlap, similarityThreshold, modelName, provider, joinThreshold

Aliases: llm, ai-semantic

Pros:

• Content-aware boundaries that align with topic changes
• Produces the highest retrieval quality in benchmarks (92-94% recall@5)
• Works on any content type without format-specific configuration
• Embedding-based similarity captures meaning that structural markers miss

Cons:

• Requires embedding API calls during chunking – significantly slower
• Costs money per document (embedding API charges)
• Depends on embedding model quality – poor embeddings produce poor splits
• Cold-start problem: first-time chunking is slow due to embedding generation

Best use case: Mixed-topic documents where structural markers do not align with topic boundaries, high-value content where retrieval quality justifies the extra cost, and any content where you need the best possible chunk quality.

Strategy 10: Semantic Markdown

Combines markdown splitting with semantic similarity to produce the best of both worlds. First splits at markdown headers (like Strategy 5), then uses embedding similarity to merge adjacent sections that are semantically related. This prevents over-fragmentation that pure markdown splitting can cause.

const result = await neurolink.generate({
  input: { text: 'How does the authentication flow work end-to-end?' },
  provider: 'openai',
  model: 'gpt-4o',
  rag: {
    files: ['./docs/auth-guide.md'],
    strategy: 'semantic-markdown',
    chunkSize: 1500,
    topK: 5,
  },
});

// Semantic-markdown-specific options:
// similarityThreshold: 0.7 -- threshold for merging adjacent sections
// maxMergeSize: 2000 -- maximum size after merging
// preserveMetadata: true -- keep markdown header metadata

Configuration options: maxSize, overlap, similarityThreshold, maxMergeSize, preserveMetadata

Aliases: semantic-md, smart-markdown

Pros:

• Structural awareness from markdown splitting plus semantic awareness from embedding similarity
• Merges related short sections into richer chunks (reduces fragmentation)
• Header metadata is preserved for faceted search
• Best choice for documentation where sections vary greatly in length

Cons:

• Requires embedding API calls (same cost concern as pure semantic)
• Only works with markdown content
• More complex configuration with two sets of parameters (markdown + semantic)

Best use case: Knowledge bases built from markdown documentation where some sections are very short and would produce low-quality chunks if split independently. The semantic merging produces denser, more information-rich chunks.

Comparison Table

#	Strategy	Speed	Quality	Cost	Chunk Consistency	Best For
1	Character	Fastest	Lowest	Free	Fixed size	Raw text, logs
2	Recursive	Fast	Good	Free	Variable	General documents
3	Token	Fast	Good	Free	Token-precise	Embedding budget control
4	Sentence	Fast	Good	Free	Sentence-aligned	Q&A, prose
5	Markdown	Fast	High	Free	Section-aligned	Documentation
6	HTML	Fast	High	Free	DOM-aligned	Web content
7	JSON	Fast	High	Free	Structure-aligned	API data, configs
8	LaTeX	Fast	High	Free	Section-aligned	Academic papers
9	Semantic	Slow	Highest	Embedding API	Topic-aligned	Mixed-topic docs
10	Semantic MD	Slow	Highest	Embedding API	Section + topic	Documentation KB

Benchmark results (500 documents, 500 queries, hybrid search + Cohere reranking):

Strategy	Recall@5	Precision@5	Avg Latency (chunk)	Avg Chunk Size
Character	71%	58%	2ms	1000 chars
Recursive	82%	71%	3ms	850 chars
Token	80%	69%	4ms	512 tokens
Sentence	83%	72%	5ms	780 chars
Markdown	88%	79%	4ms	920 chars
HTML	87%	78%	6ms	880 chars
JSON	85%	76%	5ms	750 chars
LaTeX	86%	77%	5ms	950 chars
Semantic	93%	86%	450ms	1100 chars
Semantic MD	94%	87%	480ms	1050 chars

Decision Framework

Use this flowchart to choose the right strategy for your content:

flowchart TD
    START["What is your<br/>content format?"] --> FORMATContent format?

    FORMAT -->|"Markdown"| MD_QNeed semantic<br/>merging?
    MD_Q -->|"Yes"| SEM_MD["Semantic Markdown<br/>(Strategy 10)"]
    MD_Q -->|"No"| MD["Markdown<br/>(Strategy 5)"]

    FORMAT -->|"HTML"| HTML["HTML<br/>(Strategy 6)"]
    FORMAT -->|"JSON"| JSON["JSON<br/>(Strategy 7)"]
    FORMAT -->|"LaTeX"| LATEX["LaTeX<br/>(Strategy 8)"]
    FORMAT -->|"Source code"| CODE_QWhich language?
    CODE_Q -->|"Any"| REC["Recursive<br/>(Strategy 2)"]

    FORMAT -->|"Plain text"| QUALITYQuality vs<br/>speed priority?
    QUALITY -->|"Speed"| BUDGETNeed token<br/>budget control?
    BUDGET -->|"Yes"| TOKEN["Token<br/>(Strategy 3)"]
    BUDGET -->|"No"| PROSENatural language<br/>prose?
    PROSE -->|"Yes"| SENT["Sentence<br/>(Strategy 4)"]
    PROSE -->|"No"| CHAR["Character<br/>(Strategy 1)"]

    QUALITY -->|"Quality"| SEM["Semantic<br/>(Strategy 9)"]

    FORMAT -->|"Unknown or mixed"| REC2["Recursive<br/>(Strategy 2)"]

Quick decision rules:

• Do not know the format? Start with recursive. It handles most content types well.
• Markdown documentation? Use markdown. If sections are short and fragmented, upgrade to semantic-markdown.
• Need the best quality and can afford latency? Use semantic.
• Strict token budget? Use token with the tokenizer matching your embedding model.
• Q&A knowledge base with prose? Use sentence.

Custom Chunkers: Extending the Registry

The ChunkerRegistry supports registering custom chunking strategies at runtime. Implement the Chunker interface and register your factory function before using the strategy.

import { ChunkerRegistry } from '@juspay/neurolink';
import type { Chunker, Chunk } from '@juspay/neurolink';
import { randomUUID } from 'crypto';

// 1. Implement the Chunker interface
class CodeChunker implements Chunker {
  readonly strategy = 'code' as const;

  async chunk(text: string, config?: { maxSize?: number }): Promise<Chunk[]> {
    const maxSize = config?.maxSize ?? 2000;
    const chunks: Chunk[] = [];
    const documentId = randomUUID();

    // Split at function/class boundaries
    const functionPattern = /^(?:export\s+)?(?:async\s+)?(?:function|class|const\s+\w+\s*=)/gm;
    const boundaries: number[] = [0];
    let match: RegExpExecArray | null;

    while ((match = functionPattern.exec(text)) !== null) {
      if (match.index > 0) {
        boundaries.push(match.index);
      }
    }
    boundaries.push(text.length);

    // Create chunks from boundaries
    for (let i = 0; i < boundaries.length - 1; i++) {
      const chunkText = text.slice(boundaries[i], boundaries[i + 1]).trim();
      if (chunkText.length > 0) {
        chunks.push({
          id: randomUUID(),
          text: chunkText,
          metadata: {
            documentId,
            chunkIndex: i,
            startPosition: boundaries[i],
            endPosition: boundaries[i + 1],
            documentType: 'code',
            custom: {},
          },
        });
      }
    }

    return chunks;
  }

  validate(config: unknown) {
    return { valid: true, errors: [] };
  }
}

// 2. Register with the static registry (factory function, no metadata param)
ChunkerRegistry.register('code', () => new CodeChunker());

// 3. Use it like any built-in strategy
const sourceCode = 'function hello() { return "world"; }\n// ... more code';
const chunker = ChunkerRegistry.get('code');
const chunks = await chunker.chunk(sourceCode);

The custom chunker is immediately available through ChunkerRegistry.get() and appears in getAvailableStrategies(). It participates in the same factory pattern and lifecycle management as the built-in strategies.

Combining Chunking with Hybrid Search

Chunking is only the first stage. For production RAG, combine your chosen strategy with hybrid search and reranking for the best retrieval quality:

import { RAGPipeline } from '@juspay/neurolink';

const pipeline = new RAGPipeline({
  embeddingModel: { provider: 'openai', modelName: 'text-embedding-3-small' },
  generationModel: { provider: 'openai', modelName: 'gpt-4o' },
  chunkingStrategy: 'semantic-markdown',
  chunkingConfig: {
    maxSize: 1000,
    similarityThreshold: 0.7,
    preserveMetadata: true,
  },
  searchStrategy: 'hybrid',
  hybridOptions: {
    vectorWeight: 0.6,
    bm25Weight: 0.4,
    fusionMethod: 'rrf',
  },
  reranker: {
    type: 'cohere',
    model: 'rerank-english-v3.0',
    topN: 5,
  },
});

await pipeline.ingest(['./docs/**/*.md']);

const response = await pipeline.query(
  'How do I implement rate limiting with Redis?'
);
console.log(response.answer);
console.log(`Sources: ${response.sources.length}`);

Practical Tips

Start with recursive, measure, then optimize. The recursive strategy handles 80% of use cases well. Only switch to a format-specific strategy after you measure retrieval quality and find that chunk boundaries are causing problems.

Overlap is not free. Overlap increases the total number of chunks and embedding costs. Start with zero overlap and add it only if you see retrieval failures at chunk boundaries. For sentence-based chunking, 1-2 sentences of overlap is usually sufficient.

Match chunk size to your embedding model. If your embedding model has a 512-token limit, set your token chunker to 512 tokens. If your model supports 8192 tokens, you can use larger chunks – but larger chunks mean fewer chunks, which means less granular retrieval. A good starting point is 500-1000 tokens for most embedding models.

Use metadata for faceted search. Markdown and HTML chunkers include structural metadata (header text, tag names) in each chunk. Use this metadata to narrow your search before vector similarity, dramatically improving precision.

Monitor chunk quality in production. Track the average chunk size, the number of chunks per document, and the retrieval recall for each strategy. If recall drops after a content change, the chunking strategy may need adjustment.

Conclusion

NeuroLink’s 10 chunking strategies cover every common document format and retrieval quality requirement. The ChunkerRegistry makes it straightforward to discover, configure, and switch between strategies – and the custom chunker interface lets you extend the system for domain-specific needs.

The key insight is that there is no single best chunking strategy. The best strategy depends on your content format, your quality requirements, and your latency budget. Use the decision framework to pick your starting point, measure retrieval quality with RAGAS metrics, and iterate.

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Related posts:

• Yama: AI-Native Code Review Powered by NeuroLink
• Advanced RAG: 10 Chunking Strategies, Hybrid Search, and Reranking
• How We Built the RAG Pipeline: 10 Chunking Strategies and Why