Loaders, Splitters & Embeddings — How Bad Chunking Breaks Even Perfect RAG Systems

When people debug poor RAG results, they usually blame:

• the vector database
• the embedding model
• the prompt

But in real systems, the most common root cause sits much earlier:

👉 The document ingestion pipeline

If ingestion is wrong, retrieval will be wrong — no matter how good your embeddings or LLM are.

In this article, we’ll break down:

• What a document ingestion pipeline actually is
• The role of loaders, splitters, and embeddings
• Why chunking is the most underestimated design decision
• How ingestion mistakes silently ruin RAG systems

This is concept-first thinking, with tooling examples only where helpful.

1. What Is a Document Ingestion Pipeline?

At a high level, an ingestion pipeline converts raw data into retrievable semantic units.

Raw Source
  → Documents
    → Chunks
      → Embeddings
        → Vector Store

Each stage:

• Loses information if done poorly
• Constrains everything downstream
• Is extremely hard to “fix later”

A bad ingestion pipeline doesn’t fail loudly — it fails by returning plausible but wrong answers.

2. Document Loaders: The Foundation (Often Ignored)

What loaders do

Document loaders are responsible for:

• Reading raw sources (PDFs, HTML, Markdown, APIs, DBs)
• Extracting text
• Attaching metadata

Examples of sources:

• PDFs (policies, contracts)
• Websites / wikis
• Git repositories
• Knowledge bases (Confluence, Notion)
• Databases or APIs

Common loader failures

• PDFs with broken text order
• Headers/footers mixed into content
• Navigation menus treated as text
• Missing or inconsistent metadata

If metadata is lost at this stage, you cannot recover it later.

Design rule #1

Treat metadata as first-class data.

At minimum, preserve:

• source (URL / file / system)
• page or section
• document type
• timestamp
• access scope

Frameworks (e.g. LangChain loaders) help — but you must inspect loader output manually, at least once.

3. Text Splitters: Where Most RAG Systems Break

LLMs do not retrieve documents.
They retrieve chunks.

This makes text splitting one of the most important — and misunderstood — steps in RAG.

Why splitting matters

Bad splitting causes:

• Partial facts
• Broken reasoning
• “Lost in the middle” effects
• Irrelevant or misleading retrievals

Once text is chunked incorrectly, embeddings faithfully encode the wrong thing.

4. Chunk Size Is a Trade-Off, Not a Constant

There is no universally “correct” chunk size.

Small chunks

Pros

• Higher recall
• Precise matching

Cons

• Loss of local context
• Fragmented meaning

Large chunks

Pros

• Better semantic completeness
• More context per chunk

Cons

• Fewer chunks fit in the context window
• Irrelevant content dilutes relevance

The right chunk size depends on document structure and query intent — not a blog default.

5. Why Bad Chunking Ruins Even Perfect Embeddings

This is the key misconception:

“If embeddings are good, retrieval will be good.”

Not true.

Embeddings encode what you give them.

If a chunk:

• mixes multiple topics
• cuts sentences mid-thought
• spans unrelated sections

Then the embedding becomes a semantic average — and retrieval quality collapses.

This is why:

• identical embedding models can perform wildly differently
• RAG quality varies more by ingestion than by model choice

6. Overlap: A Necessary Evil

Chunk overlap exists to:

• preserve continuity
• avoid cutting critical information

But overlap has costs:

• More chunks
• Higher storage cost
• Higher retrieval noise

Overlap should be:

• intentional
• minimal
• justified by document structure

Blindly adding overlap is not a fix — it’s a tax.

7. Embeddings Come Last (For a Reason)

Embeddings are often treated as the “magic step”.

In reality:

• They are deterministic
• They faithfully reflect upstream decisions
• They cannot repair bad ingestion

By the time text reaches the embedding stage:

• Most architectural decisions are already locked in

This is why changing the embedding model rarely fixes poor RAG results.

8. Ingestion Is an Architectural Decision

In production systems:

• Ingestion pipelines evolve over time
• Different document types need different strategies
• Re-ingestion is expensive and risky

That makes ingestion:

• a platform concern
• not a one-off script
• not a junior task

If you design ingestion casually, you pay for it forever.

9. Key Takeaways

• RAG failures usually start at ingestion
• Loaders must preserve clean text and metadata
• Chunking decisions dominate retrieval quality
• Embeddings encode mistakes faithfully
• Ingestion pipelines are architecture, not plumbing

What’s Next

In the next article, we’ll explore:

Why “Lost in the Middle” Breaks Most RAG Systems
— and why retrieving the right chunks doesn’t guarantee the model will use them.