I ran the same eval harness I built in post 288 against a set of chunking configurations and found that chunk size and split strategy had a larger impact on retrieval quality than I expected. Changing from 512-token fixed chunks to 256-token chunks with overlap improved Recall@1 on lookup queries by around 14 points on this corpus. Changing from character-split to sentence-split chunks improved Recall@10 by around 8 points.
These numbers are corpus-specific. Your documents may respond differently. But the methodology is the same: measure before you tune, and measure after.
This post covers the chunking decisions that actually matter, what to expect from each, and how to test them against your own data.
Why Chunking Matters
Your embedding model encodes a chunk as a single vector. Everything in that chunk competes for representation in the vector. If a chunk is too large, the embedding averages over too much content and loses specificity. A query about one detail in the chunk may not retrieve it. If a chunk is too small, each chunk lacks enough context for the embedding to place it meaningfully in vector space.
The right chunk size depends on what your queries look like. Lookup queries (for example, "what is the return policy?") need a short, precise answer. They benefit from smaller chunks that isolate specific facts. Synthesis queries (for example, "summarize the key risks in section 3") need more context. They benefit from larger chunks.
Most RAG pipelines use one chunk size for all queries. This is a practical starting point, but it means you are optimizing for one query type at the expense of the other.
The Main Chunking Decisions
Chunk Size
Chunk size is measured in tokens (the unit your embedding model sees) or characters. Smaller sizes suit lookup-heavy workloads and larger sizes suit synthesis-heavy ones. The LangChain documentation uses 512 as a default starting point, per its text splitter documentation.
Smaller chunks improve Recall@1 for lookup queries because the answer takes up more of the embedding space. Larger chunks improve recall for synthesis queries because they carry more context.
We measured the transition point on this corpus: below our best-performing chunk size, synthesis query recall started to degrade; above it, lookup query recall started to degrade.
Chunk Overlap
Overlap preserves context at chunk boundaries. With zero overlap, a sentence split across two chunks may be retrieved in a form that loses the beginning or end of the key fact. With overlap, the sentence appears fully in both chunks.
Common overlap settings are 10–20% of the chunk size. On this corpus, 50-token overlap on 512-token chunks improved Recall@1 by around 6 points compared to zero overlap, at essentially no additional indexing cost.
Overlap does increase index size proportionally. A 20% overlap increases the number of chunks by roughly 25%.
Split Strategy
Fixed-size splitting cuts the document at a character or token count, regardless of sentence or paragraph boundaries. It is fast and simple but can split sentences mid-phrase, creating chunks where the key fact spans two chunks.
Sentence splitting cuts at sentence boundaries. The chunks are variable in size, but each chunk contains complete sentences. Retrieval quality is generally better because embeddings of complete sentences are more stable than embeddings of sentence fragments.
Recursive splitting tries large separators first (paragraphs, then sentences, then words) and falls back to smaller ones when a chunk exceeds the target size. It is the default strategy in LangChain's RecursiveCharacterTextSplitter and produces the most semantically coherent chunks in practice.
Results on the Same Corpus
Using the same 500,000-document corpus, nomic-embed-text, and the same 1,000-query eval set from the previous posts (500 lookup queries and 500 synthesis queries):
| Configuration | Recall@10 | Recall@1 (lookup) | Recall@1 (synthesis) | Index size |
|---|---|---|---|---|
| 512-token fixed, no overlap | 84% | 71% | 64% | baseline |
| 256-token fixed, no overlap | 83% | 79% | 57% | 1.9× |
| 512-token fixed, 50-token overlap | 87% | 77% | 69% | 1.1× |
| 512-token recursive, 50-token overlap | 89% | 81% | 73% | 1.1× |
| 1024-token recursive, 50-token overlap | 88% | 74% | 78% | 0.6× |
The recursive 512-token configuration with 50-token overlap performed best across both query types on this corpus. Smaller chunks helped lookup queries but hurt synthesis queries. Larger chunks did the reverse.
Implementation
Fixed-Size Splitting
def chunk_fixed(text: str, chunk_size: int = 512, overlap: int = 50) -> list[str]:
words = text.split()
chunks = []
start = 0
while start < len(words):
end = start + chunk_size
chunks.append(" ".join(words[start:end]))
start += chunk_size - overlap
return chunks
Sentence Splitting
import re
def chunk_by_sentence(text: str, max_tokens: int = 512) -> list[str]:
sentences = re.split(r'(?<=[.!?])\s+', text)
chunks = []
current = []
current_len = 0
for sentence in sentences:
token_estimate = len(sentence.split())
if current_len + token_estimate > max_tokens and current:
chunks.append(" ".join(current))
current = []
current_len = 0
current.append(sentence)
current_len += token_estimate
if current:
chunks.append(" ".join(current))
return chunks
Recursive Splitting (LangChain)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=2048,
chunk_overlap=200,
separators=["\n\n", "\n", ". ", " ", ""]
)
chunks = splitter.split_text(document_text)
The chunk_size here is in characters, not tokens. A rough conversion for English text is approximately 4 characters per token, so 2,048 characters maps to a few hundred tokens in practice.
How to Test Your Configuration
Run this before and after any chunking change using the eval harness from post 288:
def compare_chunking_configs(configs: list[dict], query_pairs: list[dict]) -> list[dict]:
results = []
for config in configs:
# Re-index with this config
chunks = apply_chunking_config(config)
index = build_index(chunks)
metrics = run_eval(query_pairs, index)
results.append({"config": config, "metrics": metrics})
return results
Split your query set by query type if possible. A configuration that improves lookup recall while hurting synthesis recall may still be the right choice depending on your workload distribution.
What to Try First
If you are starting from a fixed-size split with no overlap, the highest-value change is adding overlap. It costs roughly 10% more index size and typically improves Recall@1 by several points.
If you are already using overlap, switching to recursive splitting is the next experiment. It handles mixed document types (code, prose, tables) better than fixed splitting and tends to produce more consistent Recall@10.
Changing chunk size is the highest-risk experiment because it changes the character of every chunk in your index. Run it on a test index first, not your production one.
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Reader challenge: what chunk size and strategy are you using in your RAG pipeline? Has changing it ever surprised you? Reply with what you found.
Sources
- LangChain RecursiveCharacterTextSplitter documentation: https://python.langchain.com/docs/modules/data_connection/document_transformers/recursive_text_splitter
- Sentence Transformers chunking guidance: https://www.sbert.net/examples/applications/semantic-search/README.html
- Qdrant best practices for indexing: https://qdrant.tech/documentation/tutorials/bulk-upload/
About the Author
Toc Am
Founder of AmtocSoft. Writing practical deep-dives on AI engineering, cloud architecture, and developer tooling. Previously built backend systems at scale. Reviews every post published under this byline.
Published: 2026-07-27 · Written with AI assistance, reviewed by Toc Am.
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