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LLM & Generative AI Systems • Chunking Strategies & Context Window ManagementHard⏱️ ~3 min

Chunking Trade-offs: When to Choose What

The Core Decision Framework:
Choosing a chunking strategy is not about finding the "best" approach. It is about matching your choice to three constraints: corpus characteristics, query patterns, and operational budget. The same system might use different strategies for different document types.
Small Chunks (150 to 300 tokens)
High precision, fit 40 to 80 chunks. Risk: lose context across boundaries
vs
Large Chunks (800 to 1,200 tokens)
Preserve context, fit 10 to 20 chunks. Risk: wasted tokens on irrelevant text
When Small Chunks Win
Use 150 to 300 token chunks when your queries are precise and documents are dense with distinct topics. For example, a medical knowledge base with thousands of drug monographs benefits from small chunks because each query targets a specific drug. Small chunks improve retrieval precision: you get exactly the relevant paragraph without dragging along unrelated sections.

The math matters here. With a 32k context window and 200 token chunks, you can fit 100 to 150 chunks after accounting for instructions and history. This diversity helps when the answer requires synthesizing information from many sources. However, small chunks fail catastrophically with cross references. If a legal document says "see section 4.2 for exceptions" and section 4.2 is in a different chunk, the model will miss the exceptions and generate incorrect answers.
When Large Chunks Win
Use 800 to 1,200 token chunks when documents have strong internal dependencies or your queries are exploratory. For example, code documentation that references imports, configuration files, and API contracts in a single explanation needs large chunks to keep everything together.

Large chunks also help with narrative documents like design docs or incident reports, where understanding requires reading several paragraphs in sequence. The trade off is reduced diversity: with 128k tokens and 1,000 token chunks, you fit only 80 to 100 chunks after other allocations. You are betting that depth on fewer sources beats breadth across many sources.
Chunk Count vs Size Trade-off (128k Context)
150 chunks
AT 200 TOKENS
40 chunks
AT 800 TOKENS
20 chunks
AT 1200 TOKENS
Overlap and Its Cost
Overlap is insurance against boundary problems but comes with real infrastructure cost. A 20 percent overlap on 500 million chunks means 100 million extra vectors to store, embed, and search. At 1,536 dimensions per vector and 4 bytes per float, that is 600 GB of additional index data.

The decision criteria: use overlap when boundary loss would cause serious errors (legal, medical, financial documents) and you can absorb the cost. Skip overlap for high volume, low stakes corpora like customer support tickets or internal chat logs where occasional boundary loss is acceptable.
Fixed vs Semantic: The Real Trade-off
Fixed length chunking is the default for systems prioritizing operational simplicity and scale. It handles 100 million documents per day without parsing complexity, produces predictable token counts for budgeting, and never fails on malformed input. Use fixed chunking when you have massive throughput requirements or highly variable document quality.

Semantic chunking is worth the complexity when answer quality directly impacts business metrics and you can invest in robust parsing infrastructure. The 5 to 15 percent quality improvement matters when you are measuring user satisfaction, support ticket deflection, or compliance accuracy. However, you need to cap maximum chunk size to prevent variable size from breaking budgets.
"The decision is not which chunking strategy is best. It is: what are my corpus characteristics, my error budget for boundary loss, and my infrastructure cost constraints? Then pick the simplest strategy that meets those requirements."

💡 Key Takeaways

✓Small chunks (150 to 300 tokens) maximize retrieval precision and diversity, fitting 100 to 150 chunks in context, but risk losing cross references and definitions that span boundaries

✓Large chunks (800 to 1,200 tokens) preserve narrative flow and dependencies, critical for code docs and legal text, but fit only 20 to 40 chunks and waste tokens on irrelevant sections

✓20 percent overlap prevents boundary loss but increases infrastructure cost proportionally: 100 million extra vectors for a 500 million chunk corpus means 600 GB more index storage

✓Fixed length chunking handles 100 million documents per day with zero parsing overhead, preferred when operational simplicity and throughput dominate quality concerns

✓Semantic chunking improves answer quality by 5 to 15 percent in evaluations, worth the 50x to 100x ingestion slowdown when quality directly impacts business metrics like support deflection

📌 Interview Tips

1Medical knowledge bases use 200 token chunks for high precision drug queries, accepting boundary risk because each monograph is self contained

2Code documentation systems use 1,000 token chunks to preserve imports, function definitions, and usage examples that must be read together

3High volume log ingestion prefers fixed 256 token chunks to process 100 million entries daily without parsing bottlenecks or variance in chunk size

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