The Chunk Size article closed on a genuine, real risk: an important idea getting awkwardly split right at a chunk boundary. This article covers the direct fix: chunk overlap.
The simple definition
Chunk overlap is the amount of text shared between consecutive chunks, so that content near a chunk boundary appears in more than one chunk rather than being cleanly cut off. If chunks are 500 tokens long with a 50-token overlap, each chunk shares its final 50 tokens with the beginning of the next chunk — a deliberate, small redundancy built into the chunking process from the Chunking article.
Why a small amount of deliberate redundancy actually helps
Recall from the Chunk Size article’s core risk: a fixed chunk boundary can fall in the middle of a sentence, or right between a question and the answer that immediately follows it, splitting one coherent idea across two separate chunks — and since retrieval, as covered in the Retrieval article, treats each chunk as a separate, independent unit, a query might only match one half of that split idea, missing crucial context the other half contained.
Chunk overlap directly mitigates this: by letting a bit of text repeat across the boundary, an idea that would otherwise be split cleanly in half is much more likely to appear complete within at least one of the two overlapping chunks.
flowchart LR
A[Chunk 1: tokens 1-500] --> C["Shared overlap: tokens 451-500"]
B[Chunk 2: tokens 451-950] --> C
C --> D[An idea spanning the boundary is likely captured whole in at least one chunk]ANALOGY vs. TECHNICAL REALITY
Analogy: Think of two overlapping photographs taken to create a panorama, where each photo deliberately captures a bit of the same scenery as its neighbor. That intentional overlap ensures nothing important gets lost in the seam between the two images — any single object near the boundary is fully visible in at least one of the two overlapping shots.
Where this breaks down: A panorama’s overlapping photos get stitched back together into one seamless image afterward.
Chunk overlap doesn’t get “stitched” the same way — the overlapping chunks remain genuinely separate, independent entries in the knowledge base, each with its own embedding, as covered in the Embedding article; the overlap simply increases the odds that important content near a boundary gets fully represented in at least one retrievable unit, rather than eliminating the boundary altogether.
How much overlap is actually appropriate
This is worth being concrete about, since it’s a real, tunable setting alongside chunk size itself. A common practical convention is setting overlap to roughly 10-20% of the chunk size — for 500-token chunks, an overlap of 50 to 100 tokens.
Too little overlap doesn’t meaningfully reduce the boundary-splitting problem; too much overlap means storing and searching through substantial amounts of duplicated content, directly increasing storage costs and the size of the knowledge base without a proportional benefit, echoing the genuine cost trade-offs covered throughout the Vector Database article.
A concrete example, layered
For a simple beginner example: chunking a recipe document with a chunk size of 200 tokens and no overlap might cut a sentence like “Bake at 350 degrees for 25 minutes” right in half, with “Bake at 350 degrees” ending one chunk and “for 25 minutes” starting the next — a small overlap ensures this full instruction appears completely intact within at least one chunk.
For a production example: chunking configuration in tools like LangChain, referenced throughout this phase, exposes chunk overlap as a standard, directly configurable parameter alongside chunk size, reflecting how routinely real RAG pipelines tune both settings together rather than treating chunk size in isolation.
Why overlap doesn’t fully solve chunking’s underlying challenge
It’s worth being honest about a genuine, remaining limitation here, not presenting overlap as a complete fix.
Chunk overlap helps specifically with content located near a boundary — it doesn’t address the deeper, more fundamental challenge of choosing genuinely good boundaries in the first place, which is precisely why more advanced approaches like the semantic chunking mentioned in the Chunking article remain an active area of ongoing improvement, rather than overlap alone being considered a fully sufficient solution.
See overlap preserve a boundary
Original tokens: 1 -------------------------------------- 180
Chunk 1: 1 ---------------- 100
Chunk 2: 81 ---------------- 180
└─ 20-token overlap ─┘If an important sentence begins near token 95 and ends near token 110, zero overlap may divide it between chunks. A 20-token overlap allows one chunk to preserve the complete sentence.
Too much overlap creates many near-duplicates, increases storage and embedding cost, and can fill the final context with repeated text. Deduplication or diversity-aware retrieval may be needed when overlapping chunks repeatedly occupy the top results.
Verified production numbers from Google
Vertex AI RAG Engine documents a default chunk_overlap of 200 tokens alongside its default 1,024-token chunk size. Neighboring chunks therefore repeat some text instead of meeting at a perfectly sharp boundary. More overlap also means more duplicate stored text, so this default should still be tested on the application’s own questions. Source: Google’s RAG transformations guide.
Common misconception
A frequent beginner assumption: that more overlap is always straightforwardly better, since it seems to reduce the risk of losing important content. As the cost discussion above explained, this isn’t quite right — excessive overlap meaningfully increases storage and retrieval cost through redundant content, without a proportional improvement in retrieval quality past a certain point, making this a genuine trade-off to tune deliberately rather than a setting to simply maximize.
Where this fits in what comes next
You now understand the complete practical chunking toolkit — chunking itself, chunk size, and chunk overlap. The next article, Retriever, zooms out to the full component responsible for actually performing the search across these prepared chunks, tying together everything covered so far in this phase.
In one sentence
Chunk overlap lets adjacent chunks share a small amount of repeated text at their boundary, directly reducing the risk of an important idea getting split cleanly in half by a chunk boundary — a real, tunable setting that trades a modest increase in storage cost for meaningfully more reliable retrieval near chunk edges.
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