Iwe adanwo Awọn ogbon Chunking

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. What does Amazon Bedrock say its default chunking approach does?
   • Splits text into roughly 300-token chunks while preserving complete sentence boundaries
   • Splits every document into one token
   • Uses one chunk for every knowledge base regardless of size
   • Groups documents by alphabetical title
2. Why can the buffer size matter in semantic chunking?
   • It changes the source document’s spelling
   • It determines how many neighboring sentences are included when creating embeddings
   • It identifies the correct speaker
   • It specifies the model’s output vocabulary
3. Kini iṣowo akọkọ laarin awọn chunks kekere pupọ ati pupọ julọ?
   • Kekere chunks nigbagbogbo losokepupo lati fi sabe
   • Awọn ege nla ko le ṣe gba pada
   • Awọn chunks kekere jẹ kongẹ ṣugbọn padanu ipo; ti o tobi chunks pa o tọ sugbon dilute ibaramu
   • Ko si iyatọ ti o nilari
4. In Amazon Bedrock, what may its parsed-content chunker preserve as logical boundaries?
   • Only individual characters
   • Image colors and crop boxes
   • The prompt’s output format
   • Pages or sections
5. How does Amazon Bedrock describe hierarchical chunking at retrieval time?
   • It retrieves child chunks and can replace them with broader parent chunks
   • It discards parent text and never returns broader context
   • It merges all documents into one vector
   • It converts every chunk to an image
6. How does Amazon Bedrock’s semantic chunking propose boundaries between sentences?
   • By sorting sentences alphabetically
   • Using sentence-distance thresholds to identify less-similar adjacent sentences
   • By splitting every document at the same character count
   • By detecting speaker identity
7. What did a 2026 chunking study find about overlap in its tested Natural Questions setup?
   • It always doubled answer accuracy
   • It eliminated all retrieval errors
   • It increased indexing cost without a measurable answer-quality benefit
   • It reduced indexing cost to zero
8. What happened when retrieved context grew beyond about 2,500 tokens in that study?
   • Accuracy rose without limit
   • All questions became exact matches
   • The retriever stopped using embeddings
   • Answer quality showed a context cliff