Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Macnaha dheer vs RAG Quiz

Is barbar dhig macnaha dheer iyo RAG ee qiimaha, daahitaanka, saxnaanta iyo cusboonaysiinta, oo ay ku jiraan saamaynta lumay ee dhexda iyo naqshadaha isku-dhafan.

Wadooyinka haga ee la xidhiidhaMacnaha Dheer Vs RAG
Su'aal 1 ee 8

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

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  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?
    • Waxay kaliya ku shaqeeyaan sawirada
    • 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