rag-chunking-strategy-advisor

name	RAG Chunking Strategy Advisor
description	Given a document type and retrieval goal, recommends the optimal chunking strategy for a RAG pipeline to minimize retrieval failures.
category	coding
tags	["rag","chunking","retrieval","embeddings","vector-search"]
author	simplyutils

RAG Chunking Strategy Advisor

What this skill does

This skill analyzes your document types, content structure, and retrieval goals to recommend the right chunking strategy for your RAG pipeline. Poor chunking is the #1 cause of RAG failures — chunks too large lose precision, chunks too small lose context. This skill picks the right strategy and explains exactly how to implement it.

How to use

Claude Code / Cline

Copy this file to .agents/skills/rag-chunking-advisor/SKILL.md in your project root.

Then ask:

• "Use the RAG Chunking Strategy Advisor to help me chunk our legal contract PDFs."
• "What chunking strategy should I use for markdown documentation with code blocks?"

Provide:

• Document type (PDFs, markdown, HTML, code, emails, etc.)
• Typical document length
• What users will search for (questions, keywords, concepts)
• Your embedding model if known

Cursor / Codex

Paste the instructions below along with your document type and retrieval use case.

The Prompt / Instructions for the Agent

When asked to advise on RAG chunking, follow these steps:

Step 1 — Identify document characteristics

Ask or infer:

• Document type: structured (tables, headers) vs. unstructured (prose) vs. code
• Length: short (< 1 page), medium (1–20 pages), long (20+ pages)
• Internal structure: does it have headers, sections, numbered lists, code blocks?
• Query type: factual lookups, conceptual questions, code search, or multi-hop reasoning?

Step 2 — Select the primary chunking strategy

Document Type	Recommended Strategy	Chunk Size
Prose (articles, books)	Sentence-window or recursive character	512–1024 tokens
Structured docs (markdown, HTML)	Header-based (split on H2/H3)	Full section
PDFs with mixed content	Semantic chunking + page boundary	512 tokens
Source code	Function/class boundary splitting	Full function
Tables / spreadsheets	Row-level or table-level	1 row or full table
Emails / short messages	Document-level (no chunking)	Full doc
Legal / contracts	Clause-level splitting	256–512 tokens

Strategies explained:

• Fixed-size with overlap — Split every N tokens with a 10–20% overlap. Fast, simple, works for uniform prose. Use chunk_size=512, overlap=50.
• Recursive character splitting — Split on \n\n, then \n, then ., then . Preserves paragraph structure. Best default for general text.
• Header-based (Markdown/HTML) — Split on heading tags (H1/H2/H3). Each section becomes a chunk with its heading prepended. Best for docs sites and wikis.
• Semantic chunking — Use an embedding model to detect topic shifts; split when cosine similarity drops. Best quality, highest cost. Use when documents have abrupt topic changes.
• Sentence-window — Store sentences as chunks but retrieve surrounding N sentences for context. Good for Q&A over dense technical content.
• Function-level (code) — Use AST parsing to split on function/class boundaries. Never split mid-function.

Step 3 — Configure chunk metadata

Every chunk must carry metadata for filtering and re-ranking:

{
  "chunk_id": "doc_001_chunk_004",
  "source": "contracts/nda-2024.pdf",
  "page": 3,
  "section": "Confidentiality Obligations",
  "doc_type": "legal",
  "created_at": "2024-11-01"
}

Always store: source, page_or_line, section_heading (if available), doc_type.

Step 4 — Add a parent-child chunk structure (for complex docs)

For long structured documents, use a two-level hierarchy:

• Parent chunks (1024–2048 tokens): stored for context retrieval
• Child chunks (128–256 tokens): used for embedding and search

Retrieve by child similarity, return parent for context. This avoids the precision vs. context tradeoff.

# LlamaIndex example
from llama_index.node_parser import HierarchicalNodeParser
parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])

Step 5 — Validate with retrieval testing

Before deploying, run this checklist:

• Sample 20 real user queries against your chunked index
• Check if the correct chunk appears in top-3 results
• Verify chunk boundaries don't cut mid-sentence or mid-table
• Ensure no chunk is under 50 tokens (too small = noise)
• Ensure no chunk is over 2048 tokens (too large = diluted embedding)

Common chunking mistakes to flag

• Splitting tables row-by-row when the header row is in a different chunk — always keep headers with data rows
• Not adding overlap on fixed-size splits — 10% overlap prevents losing context at boundaries
• Chunking code by line count instead of by function — always use AST-aware splitting for code
• Ignoring document structure — a 500-token chunk that spans two unrelated sections is worse than two 250-token single-topic chunks

Example

Input:

"Use the RAG Chunking Advisor. I'm building a support chatbot. Documents are markdown product docs, 5–30 pages each, with H2 and H3 headers. Users ask specific how-to questions."

Output:

Recommended strategy: Header-based chunking

Split on H2 headers as primary boundaries, H3 as secondary. Each chunk = one section, with its full heading path prepended (Product > Installation > macOS Setup).

Config:

from langchain.text_splitter import MarkdownHeaderTextSplitter
splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=[("##", "section"), ("###", "subsection")]
)

Add metadata: {"source": filename, "section": heading_path, "doc_type": "product_docs"}

If sections exceed 800 tokens, apply a secondary recursive split with chunk_size=600, overlap=60.

Validate: run your top 20 support questions against the index and confirm the relevant section appears in top-3.