In this blog, we break down why standard chunking fails for structured data and how to design table-preserving chunking strategies using modern RAG best practices. Each approach comes with implementation guidance, use cases, and architecture fit.

Why chunking fails on tabular data?

Tables aren’t text — they are relational knowledge graphs compressed into rows and columns. But standard chunking doesn’t know that. It simply slices text by length.

Problem	What goes wrong
Fractured Context	Tables are split mid-row, detaching values from headers
Lost Schema	Column context disappears, numbers lose meaning
Semantic Drift	LLM fills missing data, causing hallucinations
Retrieval Failure	Vector search can’t retrieve row value + meaning together

Example of a failure case:

Chunk A                     Chunk B
| Revenue | Growth |        | $10M | 15% |

Individually, neither chunk is meaningful.
The RAG system retrieves “$10M | 15%” without knowing what it represents — leading to inaccurate answers, hallucinated summaries, and broken analytics.

The solution:
Treat tables as atomic semantic units — never split without retaining headers.

Everything that follows is built on this principle.

4 proven strategies to improve RAG performance on tabular data:

1. Page-level chunking (Best for PDFs)

Use case: Financial reports, legal contracts, research papers, scanned annual reports
Why it works: Tables usually sit inside a visually bounded block.

Instead of splitting text by tokens, we chunk based on document layout, and hence a page not raw text length, becomes the unit of retrieval.

📌 In NVIDIA’s 2024 RAG Benchmarks, page-level chunking achieved:

0.648 top-1 accuracy
Lowest variance (0.107) among all chunkers
Outperformed all token-length-based methods

This matters when building AI systems for compliance, M&A analysis, taxation, or credit risk — where a missing column is the difference between correct answer vs compliance risk.

Implementation for Production (Unstructured.io)

Using high-resolution layout extraction, identify table elements distinctly:

from unstructured.partition.pdf import partition_pdf

elements = partition_pdf(
    "report.pdf",
    strategy="hi_res",
    infer_table_structure=True
)

tables = [e for e in elements if e.category == "Table"]

This keeps:

Tables intact
Captions + footnotes preserved\
Adjacent context available for reasoning

2. Recursive Splitting with table-aware separators (Best for Markdown + Notion)

Markdown tables are fragile — one naive chunk split, and the schema collapses. Recursive splitting solves this only if we teach the splitter that | is not just a symbol, it is a boundary of meaning.

Core idea:

Add the markdown row separator | as a high-priority split boundary.

Most recursive chunkers prioritize:

Paragraphs
Sentences
Tokens

We modify that hierarchy:

|  → Highest priority  
\n\n → Second  
.  → Fallback only if needed

This ensures:

Benefit	Value in RAG
Rows kept intact	No fragmented semantic units
Schema preserved	Less hallucination during synthesis
High retrieval granularity	Great for documentation & LLM notes

Ideal for:

Product documentation
SaaS knowledge bases
Notion exports
Wiki / Markdown datasets
LLM-generated structured text

3. Schema-aware semantic chunking (Best for CSV, SQL, Excel)

Database rows are meaningless alone.

Example:

45, 2023, Q2

The LLM can’t infer that 45 = Revenue (Millions) unless the schema is present.

The paper TableRAG demonstrated that:

Row embeddings with schema enrichment outperform raw table embeddings.

So instead of chunking raw CSV rows, we expand them into semantic sentences.

Pandas enrichment example:

import pandas as pd

df = pd.read_csv("finance.csv")

enriched = [
    f"Revenue={r['revenue']}M, Year={r['year']}, Quarter={r['quarter']}"
    for _, r in df.iterrows()
]

Now embeddings contain:

Meaning
Context
Queryability

This enables questions like:

“What was Q2 revenue in 2023?”

to retrieve exactly the right row without any hallucination.

4.Hierarchical chunking (Best for complex multi-format documents)

Some documents have cross-page relationships. Example: A metric definition appears in Section 4, but values appear in Appendix B Table 23. Naive RAG retrieves only one, causing incorrect analysis.

Hierarchical chunking fixes this:

Layer	Size	Purpose
Parent Chunk	Large	Full table, headers, surrounding text
Child Chunk	Small	Row-level key-value access

Retrieval Flow

Query → retrieve child chunk (precise match)
Child → triggers retrieval of parent chunk
LLM receives full context for final reasoning

This is currently one of the best patterns for:

Audit reports
Infrastructure documentation
Policy + regulatory frameworks
Research publications
Enterprise BI knowledge graphs

It maximizes:

Precision
Recall
Reasoning quality

The trifecta for RAG excellence.

If your enterprise RAG system handles:

Financial or compliance documents
Operational metrics + dashboards
SQL / Excel / BI exports
Regulatory filings
Research + scientific data

Then your chunking strategy will determine your model accuracy, not your model size.

Why chunking fails on Tables in RAG, & 4 proven strategies to fix it