Contextual Retrieval Quiz

Answers and explanations appear as you take the quiz.

1. What problem does contextual retrieval solve?
   • Chunks lose information like company or time period when split from their document
   • Embeddings are too expensive to store
   • Language models cannot read tables
   • BM25 cannot handle long queries
2. How is the context for each chunk generated?
   • By copying the document title only
   • A language model reads the whole document and the chunk and writes a short situating explanation
   • By a human editor for every chunk
   • By averaging neighboring embeddings
3. Which two indexes receive the contextualized chunk text in Anthropic's approach?
   • A graph database and a cache
   • A SQL table and a spreadsheet
   • An embedding index and a BM25 keyword index
   • Two separate vector indexes with different models
4. According to Anthropic's reported experiments, roughly how much did contextual embeddings plus contextual BM25 reduce top-20 retrieval failures?
   • About 5 percent
   • About 20 percent
   • About 90 percent
   • About 49 percent
5. Why does prompt caching make contextual retrieval cheaper?
   • It caches the document once and reuses it for every chunk's context call
   • It skips the embedding step
   • It removes the need for BM25
   • It shortens each chunk
6. Why is keeping BM25 in the pipeline especially useful with added context?
   • BM25 understands synonyms better than embeddings
   • Contexts often add exact identifiers like names, dates or codes that keyword search matches precisely
   • BM25 requires no index
   • BM25 replaces reranking
7. When might contextual retrieval be unnecessary?
   • When the documents are in English
   • When using a reranker
   • When the knowledge base is small enough to fit in the model's context window
   • When chunks are longer than 50 tokens
8. Where does the main extra cost of contextual retrieval occur?
   • At query time for every user question
   • In the reranker only
   • In the user's browser
   • At indexing time, with one model call per chunk