Rag Chunking Optimizer

RAG Chunking Optimizer

Analyze documents and recommend optimal chunking strategies for RAG (Retrieval-Augmented Generation) pipelines. Evaluates chunk sizes, splitting methods, overlap settings, metadata enrichment, and retrieval quality. Use when building or optimizing RAG applications.

Usage

"Optimize chunking for my knowledge base documents"
"What's the best chunk size for these technical docs?"
"Analyze my current chunking strategy for retrieval quality"
"Help me set up semantic chunking for my RAG pipeline"
"Compare chunking strategies for my document types"

How It Works

1. Document Analysis

Profile the document corpus:

# Analyze document types and sizes
find docs/ -type f \( -name "*.md" -o -name "*.txt" -o -name "*.pdf" -o -name "*.html" \) -exec wc -l {} + | sort -rn | head -20
Check document structure patterns
for f in docs/*.md; do
  echo "=== $f ==="
  grep -c "^#" "$f"           # heading count
  grep -c "^

Classify documents by type:
Structured technical docs: APIs, references, manuals → heading-based splitting
Narrative content: articles, blog posts, reports → semantic/paragraph splitting
Code-heavy docs: tutorials, examples → code-aware splitting
Tabular data: specs, configurations → row/section splitting
Q&A / FAQ: question-answer pairs → pair-based splitting
Legal/compliance: contracts, policies → clause-based splitting
2. Chunking Strategy Evaluation
Evaluate strategies against the corpus:
Fixed-size chunking:
Pros: Simple, predictable, works everywhere
Cons: Breaks mid-sentence, loses context
Best for: Homogeneous documents, initial prototyping
Typical: 512-1024 tokens, 50-100 token overlap
Recursive character splitting:
Split hierarchy: \n\n → \n → .  →   → `

Pros: Respects natural boundaries, widely supported
Cons: May still break semantic units
Best for: General purpose, mixed content
Semantic chunking:
Group sentences by embedding similarity
Pros: Preserves meaning, variable-size chunks
Cons: Slower, requires embedding model, harder to debug
Best for: Narrative content, complex topics
Heading-based (markdown/HTML):
Split on heading hierarchy (H1 → H2 → H3)
Pros: Preserves document structure, natural sections
Cons: Uneven chunk sizes, may be too large or small
Best for: Structured documentation, wikis
Code-aware chunking:
Split on function/class boundaries using AST
Pros: Complete code units, preserves imports/context
Cons: Language-specific, requires parsing
Best for: Code documentation, API references
Sliding window with context:
Overlapping windows with parent/sibling context
Pros: Never loses boundary context
Cons: Storage overhead, retrieval deduplication needed
Best for: Dense technical content
3. Chunk Size Optimization
Factors that determine optimal chunk size:
Embedding model: Match chunk size to model's sweet spot
  - text-embedding-3-small: 256-512 tokens optimal
  - text-embedding-ada-002: 512-1024 tokens
  - voyage-3: 512-1024 tokens
  - BAAI/bge-large: 256-512 tokens
Query type: Short queries → smaller chunks; complex queries → larger
Answer density: If answers span paragraphs → larger chunks
Context window: LLM context limits how many chunks you can inject
Latency budget: More chunks = more embedding comparisons
4. Overlap Analysis
Determine optimal overlap:
Too little overlap (0-10%): Context lost at boundaries, retrieval misses
Optimal overlap (10-20%): Maintains context without excessive duplication
Too much overlap (>25%): Storage waste, retrieval returns near-duplicates
5. Metadata Enrichment
Recommend metadata to attach to each chunk:
Source metadata: file path, section heading, page number
Structural metadata: document type, heading hierarchy, position
Semantic metadata: extracted entities, keywords, topic classification
Temporal metadata: creation date, last modified, version
Relational metadata: links to parent/child/sibling chunks
6. Quality Metrics
Evaluate chunking quality:
Semantic coherence: Does each chunk contain a complete thought?
Retrieval precision: Top-K results contain the answer?
Retrieval recall: Can every answerable question find its chunk?
Chunk size distribution: Normal distribution around target?
Boundary quality: How often are sentences/concepts split?
Deduplication ratio: How much content overlaps between chunks?
7. A/B Testing Framework
Design experiments to compare strategies:

Output

# Primary strategy: heading-based for structured docs
structured_splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=[("##", "Section"), ("###", "Subsection")],
    strip_headers=False
)
Fallback: recursive for unstructured content
recursive_splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,
    chunk_overlap=64,
    separators=["\n\n", "\n", ". ", " "]
)

"Optimize chunking for my knowledge base documents"
"What's the best chunk size for these technical docs?"
"Analyze my current chunking strategy for retrieval quality"
"Help me set up semantic chunking for my RAG pipeline"
"Compare chunking strategies for my document types"