AI0-001 Implementing AI Solutions Practice Question
A team is implementing a RAG system for legal document retrieval. The documents are long and cover multiple topics. Which chunking strategy is MOST appropriate to ensure each chunk contains coherent information?
⚠ Common exam trap
CompTIA often tests the misconception that fixed-size token chunking is always optimal for simplicity, but in domain-specific RAG systems with long, multi-topic documents, semantic boundaries are essential to maintain chunk coherence and retrieval accuracy.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Semantic chunking based on topic boundaries
Semantic chunking based on topic boundaries is the most appropriate strategy because legal documents are long and cover multiple topics. By splitting at natural topic shifts (e.g., clauses, sections, or argument transitions), each chunk preserves coherent meaning, which is critical for accurate retrieval and generation in a RAG system. This approach avoids mixing unrelated content within a single chunk, which would degrade the quality of retrieved context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Hierarchical chunking with overlapping windows
Why it's wrong here
Hierarchical chunking with overlapping windows preserves document structure but fails here because legal documents require each chunk to be semantically self-contained; overlapping windows introduce redundant content across chunks, which dilutes coherence and risks splitting a single legal argument across two chunks. This approach is tempting for general long-document retrieval where preserving context across boundaries matters, such as in academic papers, where overlapping windows maintain flow between sections.
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Semantic chunking based on topic boundaries
Why this is correct
Semantic chunking splits where embedding similarity between adjacent sentences drops, so boundaries fall at genuine topic shifts rather than arbitrary token counts. Long, multi-topic legal documents therefore yield chunks that each stay within one coherent subject.
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Fixed-size chunking with 512 tokens
Why it's wrong here
Fixed-size chunking splits text at a token count regardless of sentence or topic boundaries, so a 512-token window can cut mid-clause and merge unrelated subjects, breaking the coherence the scenario demands. It is tempting because it is simple, predictable and cheap for uniform, single-topic text where boundary alignment does not matter.
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Character-level chunking with no overlap
Why it's wrong here
Character-level chunking destroys word and sentence boundaries, making retrieval nearly impossible.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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