Most teams spend weeks choosing the right LLM. The right vector database. The right embedding model. Then they split their documents every 500 characters and wonder why the answers are bad.
Chunking is the most overlooked decision in AI development — and it's quietly responsible for more RAG failures than any other factor.
The Decision Nobody Talks AboutThere's a pattern in almost every RAG implementation that doesn't perform well. The team chose a capable LLM. They picked a solid vector database. They spent time on prompt engineering. But the answers are still inconsistent — sometimes good, often missing the point, occasionally confidently wrong.
The culprit is almost always chunking.
Chunking — how you split your source documents into pieces before generating embeddings — is the most consequential and least discussed decision in RAG architecture. Get it right and your retrieval surfaces exactly what the model needs. Get it wrong and no amount of prompt tuning, model upgrades, or infrastructure investment will fix the underlying problem.
Why Chunking Determines Retrieval QualityWhen a user submits a query, your RAG system doesn't search the full document. It embeds the query into a vector, then finds the stored chunks whose vectors are most similar. Those chunks — and only those chunks — become the context the LLM reasons from.
This means the quality of every answer is bounded by the quality of what retrieval returns. And what retrieval returns is entirely determined by how you chunked.
Two failure modes dominate:
Chunks too large — The retrieved context is noisy. A 2,000-character chunk might contain the answer your user needs, but it's buried alongside unrelated sentences that dilute the semantic signal. The model gets confused, hedges, or drifts toward the louder parts of the context.
Chunks too small — The retrieved context loses coherence. A 100-character chunk might contain a key phrase but not enough surrounding meaning to be useful. The model gets fragments instead of ideas, and answers feel incomplete or disconnected from what the user actually asked.
There's a third, subtler failure: chunk boundaries that cut through key ideas. If a critical concept spans a paragraph boundary and your chunker splits it in half, neither half is retrievable as a meaningful unit. The most relevant piece in your entire knowledge base never surfaces — not because retrieval failed, but because the piece was never a retrievable unit to begin with.
The Three Chunking Strategies Worth KnowingFixed-size chunking
Split every N characters (or tokens), with or without overlap between consecutive chunks. This is the default in most RAG tutorials and the most commonly used strategy in practice. It's fast, simple, and predictable. For uniform, structured data — product catalogs, database exports, short-form records — it works reasonably well.
The problem is prose. Natural language doesn't respect fixed character boundaries. A sentence that starts at character 498 and ends at character 531 gets split across two chunks. One chunk ends mid-thought; the next starts mid-thought. Both are semantically weaker than the complete sentence.
Fixed-size chunking with overlap (repeating the last N characters of each chunk at the start of the next) partially mitigates this — but it's a workaround for a structural mismatch, not a solution.
Sentence-based chunking
Split on natural language boundaries — sentence endings, paragraph breaks — rather than arbitrary character counts. This preserves semantic coherence at the unit level. Each chunk is a complete thought. Retrieval finds complete thoughts. The model reasons from complete thoughts.
Sentence-based chunking works well for most document types: technical documentation, blog articles, support content, knowledge base articles, legal text. The chunks are variable in size, which means some will be larger than others — but they're semantically valid, which is what matters for retrieval quality.
The tradeoff is that very long paragraphs become very large chunks, and very short sentences become very small ones. Some post-processing to merge short chunks and split unusually long ones improves results significantly.
Recursive chunking
Split hierarchically: paragraphs first, then sentences within paragraphs that exceed a size threshold, then clauses or words if needed.
Recursive chunking is the most sophisticated of the three and consistently produces the best retrieval quality for mixed-format content — documents that combine headings, paragraphs, lists, code blocks, and tables. It respects the document's natural structure rather than imposing an artificial one.
The cost is implementation complexity. Recursive chunking requires a parser that understands document structure, not just character boundaries. For teams working with well-formatted documents (Markdown, HTML, structured PDFs), this is straightforward. For raw text or inconsistently formatted sources, it requires more preprocessing.
Chunk Size: What the Numbers Actually Tell YouChunk size interacts with chunking strategy and embedding models in ways that aren't always intuitive. A few principles that hold across most use cases:
● Smaller chunks (100–300 tokens) produce higher retrieval precision — the retrieved unit is more tightly focused on the query. But they lose context. An answer that requires understanding a multi-sentence explanation won't be well-served by fragments. ● Larger chunks (500–1,000 tokens) preserve more context per retrieved unit. But they introduce noise and dilute the semantic signal, which reduces retrieval precision.
Most production RAG systems land between 256 and 512 tokens per chunk, with sentence-based or recursive splitting. This range balances precision and context for the majority of use cases.
The right number for your system isn't derivable from first principles — it requires evaluation against your actual data and your actual queries.
What TecoFize RecommendsAt TecoFize, we treat chunking strategy as a first-class architectural decision in every RAG system we build — not a default to accept and move on from.
Our starting point for most document-heavy use cases is sentence-based or recursive chunking at 300–512 tokens, with overlap, evaluated against a representative query set before any other optimisation. We've consistently found that improving chunking strategy delivers more retrieval quality improvement than switching embedding models or increasing top-k — at a fraction of the effort.
If you're building a RAG-powered feature and your answers aren't as good as your model should be capable of, chunking is the first place to look.
TecoFize delivers end-to-end digital transformation and automated AI development for startups and growing businesses across the USA, Middle East, and Europe.
