Overlap

What Is Chunk Overlap?

Chunk overlap means repeating a small amount of content from the end of one chunk at the beginning of the next chunk. The repeated context helps information that sits near a chunk boundary remain available during retrieval.

Here, sentence 5 appears in both chunks. That repeated sentence is the overlap.

Why Does Overlap Matter?

Important information does not always stop exactly where a chunk stops. A sentence can introduce a topic while the next sentence contains the answer.

How Overlap Works

Suppose you create chunks of 100 words with an overlap of 20 words.

Overlap in a Fixed-size Chunker

For a simple word-based splitter, overlap can be implemented with a step smaller than the chunk size.

def chunk_with_overlap(words, chunk_size=10, overlap=2):
    if overlap >= chunk_size:
        raise ValueError("overlap must be smaller than chunk_size")

    chunks = []
    step = chunk_size - overlap

    for start in range(0, len(words), step):
        chunk = words[start:start + chunk_size]
        if not chunk:
            break
        chunks.append(chunk)

        if start + chunk_size >= len(words):
            break

    return chunks


words = "RAG retrieves useful context before generation".split()
chunks = chunk_with_overlap(words, chunk_size=5, overlap=2)

for i, chunk in enumerate(chunks, start=1):
    print(i, chunk)

The important relationship is step = chunk size − overlap. Increasing overlap makes the next chunk start earlier.

How Much Overlap Should You Use?

There is no universal overlap value. It depends on chunk size, document structure, retrieval behavior, and the amount of context needed around boundaries.

These are starting points, not rules. Test them on your own documents and retrieval questions.

Overlap and Sentence Chunking

Sentence-based chunking can overlap by repeating the final one or more sentences instead of an arbitrary number of characters.

def sentence_chunks(sentences, size=3, overlap=1):
    step = size - overlap
    chunks = []

    for start in range(0, len(sentences), step):
        chunk = sentences[start:start + size]
        if not chunk:
            break
        chunks.append(chunk)

        if start + size >= len(sentences):
            break

    return chunks


sentences = [
    "Embeddings represent meaning as vectors.",
    "A vector database stores those vectors.",
    "Similarity search finds nearby vectors.",
    "RAG retrieves relevant chunks.",
    "The language model uses that context."
]

for i, chunk in enumerate(sentence_chunks(sentences), start=1):
    print(f"Chunk {i}:", chunk)

Overlap With Recursive Chunking

Recursive chunking and overlap solve different problems. Recursive splitting chooses useful boundaries; overlap repeats context across neighboring final chunks.

Overlap in RAG

In a RAG system, chunks are embedded and stored for retrieval. If a user's question depends on information near a boundary, overlap can increase the chance that the retrieved chunk contains enough context.

Overlap Does Not Fix Bad Chunking

Overlap is not a replacement for good chunk boundaries. If a chunk contains unrelated topics, repeating its ending into the next chunk can simply duplicate unrelated information.

Choose a sensible chunking strategy first. Then use overlap only where it improves boundary continuity.

The Cost of Too Much Overlap

Overlap and Retrieval Quality

Evaluate overlap using the same retrieval questions you care about in production. Compare different overlap settings rather than assuming more overlap is better.

When Is Overlap Especially Useful?

• Explanations: one sentence may define a concept and the next sentence may explain it.
• Technical documentation: setup instructions and constraints can span a chunk boundary.
• Long-form prose: ideas often continue across adjacent sentences or paragraphs.
• Q&A documents: a question and its answer can be separated by a boundary.

Common Mistakes

Overlap With Different Chunking Strategies