Title: Quiz 5 — Chunking Strategies for Real-World Documents
Course: AIINFRA 301: Production RAG & LLMOps — Observability and Evaluation > Section: Week 05 — Chunking Strategies for Real-World Documents

Q: Which chunking strategy splits purely by character/token count, without regard to sentence or paragraph boundaries?
- Recursive chunking
- Semantic chunking
- Fixed-size chunking
- Late chunking
A: Fixed-size chunking

Q: RecursiveCharacterTextSplitter tries separators in which order by default?
- It picks a random separator each time
A: It picks a random separator each time

Q: RecursiveCharacterTextSplitter is the default, general-purpose chunking strategy recommended as a safe starting point in both LangChain and LlamaIndex as of 2026.
- True
- False
A: True

Q: LlamaIndex's SemanticSplitterNodeParser decides where to insert a chunk boundary based on what signal?
- A fixed character count
- A drop in similarity between consecutive sentence embeddings that exceeds a percentile threshold
- The presence of HTML tags
- The document's file size
A: A drop in similarity between consecutive sentence embeddings that exceeds a percentile threshold

Q: Name one type of document/corpus for which semantic chunking is specifically worth its extra cost, according to this week's guidance.
Feedback: Sample/expected answer: Research papers, legal documents, or dense technical documentation (any one is acceptable)

Q: Bigger chunks are always the safer choice because they always provide more context to the model.
- True
- False
A: False

Q: What is a reasonable starting point range for chunk size and overlap discussed this week?
- 20–50 tokens with 1–2% overlap
- 200–500 tokens with 10–20% overlap
- 2,000–5,000 tokens with 50% overlap
- There is no reasonable starting point; it must be discovered from scratch every time
A: 200–500 tokens with 10–20% overlap

Q: According to 2026 guidance discussed this week, name one technique that tends to deliver bigger retrieval-quality gains than continuing to tune chunk size and overlap.
Feedback: Sample/expected answer: Contextual retrieval or late chunking (either answer is acceptable)