Why Most RAG Systems Fail

Most Retrieval-Augmented Generation (RAG) systems do not fail because of the LLM.

They fail because of bad chunking.

If your retrieval results feel:

  • Random
  • Hallucinated
  • Incomplete
  • Loosely related to the query

Then your embedding model and vector database are probably fine.

Your chunking strategy is the real bottleneck.

Chunking determines what the model is allowed to know. If the chunks are wrong, retrieval quality collapses — no matter how good the LLM is.

Below are 10 chunking strategies every serious RAG builder should understand, along with when and why to use them.

1. Fixed-Size Chunking

What it is
Splitting documents into chunks of a fixed token or character size.

Pros

  • Simple to implement
  • Fast preprocessing
  • Predictable chunk sizes

Cons

  • Frequently breaks semantic meaning
  • Sentences and concepts get cut in half

Use when

  • Prototyping
  • Data has weak structure
  • Latency matters more than accuracy

2. Overlapping Chunking

What it is
Fixed-size chunks with overlapping tokens between adjacent chunks.

Pros

  • Preserves context across boundaries
  • Strong baseline for most RAG systems

Cons

  • Increases storage and embedding cost
  • Redundant information

Use when

  • You want a safe improvement over fixed-size chunking
  • Documents are mostly linear text

3. Sentence-Based Chunking

What it is
Chunks are built from complete sentences instead of raw tokens.

Pros

  • Preserves semantic coherence
  • Better retrieval precision

Cons

  • Sentence length variability
  • Requires sentence boundary detection

Use when

  • Natural language text (articles, documentation, emails)
  • Question-answering use cases

4. Paragraph-Based Chunking

What it is
Chunks align with paragraph boundaries.

Pros

  • Matches author intent
  • High semantic density per chunk

Cons

  • Paragraphs can be too large or too small
  • Less control over token count

Use when

  • Blogs
  • Reports
  • Well-structured prose

5. Semantic Chunking

What it is
Splits text when semantic meaning shifts, typically using embeddings or similarity scores.

Pros

  • Very high retrieval quality
  • Chunks align with conceptual boundaries

Cons

  • Expensive to compute
  • Harder to tune correctly

Use when

  • Accuracy is critical
  • Dataset size is manageable
  • Queries are complex or abstract

6. Recursive Chunking

What it is
Hierarchical splitting:
Document → section → subsection → paragraph → sentence

Pros

  • Maintains document structure
  • Flexible chunk sizing

Cons

  • More complex ingestion pipeline

Use when

  • Large, nested documents
  • Technical manuals
  • Legal or regulatory content

7. Section-Based Chunking

What it is
Chunks are aligned with headings, clauses, or explicit document sections.

Pros

  • Excellent semantic alignment
  • Improves metadata filtering

Cons

  • Depends on clean document structure

Use when

  • PDFs
  • RFPs
  • Policies
  • Specifications

8. Sliding Window Chunking

What it is
A moving window scans the document, ensuring every token appears in multiple chunks.

Pros

  • Zero information loss
  • Strong recall guarantees

Cons

  • Very high redundancy
  • Expensive at scale

Use when

  • Long documents
  • Missed context is unacceptable
  • Recall > cost

9. Hierarchical Chunking

What it is
Store multiple representations:

  • High-level summaries
  • Mid-level sections
  • Fine-grained chunks

Pros

  • Enables multi-level retrieval
  • Supports query intent matching

Cons

  • Complex storage and retrieval logic

Use when

  • Large knowledge bases
  • Exploratory queries
  • Mixed high-level and detailed questions

10. Metadata-Aware Chunking

What it is
Chunks enriched with metadata such as:

  • Title
  • Section name
  • Document type
  • Source
  • Timestamp

Pros

  • Smarter filtering
  • Better reranking
  • Strong enterprise performance

Cons

  • Requires disciplined ingestion

Use when

  • Multi-source systems
  • Enterprise RAG
  • Compliance or auditability matters

Key Takeaway

There is no single “best” chunking strategy.

There is only:

  • The right chunking for your data
  • The right chunking for your query types
  • The right chunking for your latency and cost constraints

Most high-quality RAG systems use multiple chunking strategies simultaneously, combined with reranking and query-aware retrieval.

If retrieval feels broken, fix chunking first — not the model.