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Pytanie 1 z 8

Why does the long-context approach usually cost more per query than RAG?

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  1. Why does the long-context approach usually cost more per query than RAG?
    • Providers charge per input token, and long context sends far more tokens
    • Long-context models require a vector database
    • RAG is always free
    • Long context requires fine-tuning for each question
  2. What did the 2023 Lost in the Middle study find?
    • Models ignore the first sentence of every prompt
    • Retrieval always beats long context
    • Performance dropped when relevant information was placed in the middle of long inputs
    • Context windows cannot exceed 4,000 tokens
  3. What is a key limitation of needle-in-a-haystack tests?
    • Działają tylko na obrazach
    • They measure simple lookup, not reasoning across many scattered facts
    • They cannot be run on long inputs
    • They measure cost, not accuracy
  4. Which situation most clearly favors RAG over long context?
    • A single short policy document queried a few times
    • Summarizing the themes of one novel
    • Comparing two chapters of one report
    • A large, frequently changing corpus with per-user access permissions
  5. When using prompt caching with a long corpus, how should the prompt be arranged?
    • Put the question first and the corpus last
    • Shuffle the corpus on each request
    • Put the stable corpus first and the variable question last
    • Split the corpus across separate requests
  6. Why does time to first token increase with context length?
    • The model must process every input token before it starts answering
    • Longer prompts are sent over slower networks
    • The model retrieves documents from the internet
    • Output tokens are generated in parallel
  7. Which task is long context particularly well suited to?
    • Searching millions of support articles
    • Serving users with different clearance levels
    • Answering from data that changes every hour
    • Summarizing themes across an entire document
  8. What does a common hybrid of long context and RAG look like?
    • Using RAG only for images and long context only for text
    • Retrieving relevant documents generously, then including whole documents in a long window
    • Fine-tuning the model on the retrieved chunks
    • Alternating approaches on odd and even days