Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Qiyaasaha Qiimaynta RAG iyo Kediska RAGAS

Hubi fahamkaaga qiimeynta RAG: daacadnimada, ku habboonaanta jawaabta, saxnaanta macnaha guud iyo xasuusinta, iyo sida garsoorayaasha LLM u dhaliyaan.

Wadooyinka haga ee la xidhiidhaMetrics Qiimaynta RAG iyo Ragas
Su'aal 1 ee 8

Halbeegkee baa hubinaya in sheegasho kasta oo jawaabta ku jirta ay taageerto macnaha guud ee la soo saaray?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Halbeegkee baa hubinaya in sheegasho kasta oo jawaabta ku jirta ay taageerto macnaha guud ee la soo saaray?
    • Answer relevance
    • Context recall
    • Faithfulness
    • Context precision
  2. Halbeegyadan keebaa u baahan jawaab tixraac-xaraf ah oo su'aal kasta oo imtixaan ah?
    • Context recall
    • Faithfulness
    • Answer relevance
    • Response latency
  3. Maxay cabbiraysaa saxnaanta macnaha guud?
    • How quickly chunks are retrieved
    • Whether the answer is short
    • How many documents are in the index
    • Whether the relevant retrieved chunks are ranked near the top
  4. Sidee ayay RAGAS u qiyaastaa ku habboonaanta jawaabta?
    • By counting words shared by answer and context
    • By generating questions the answer would respond to and comparing them to the original question with embeddings
    • By asking the user to rate each answer
    • By checking the answer against a reference with exact string match
  5. Nidaamku wuxuu muujinayaa dib u xasuusinta macnaha sare laakiin saxnaanta macnaha hoose. Maxay u badan tahay in la hagaajiyo?
    • Improve ranking, for example by adding a reranker
    • Switch to a smaller generation model
    • Remove the reference answers
    • Lower the number of test questions
  6. Why can a perfectly faithful answer still be wrong?
    • Faithfulness only checks grammar
    • Faithfulness ignores the retrieved context
    • It can faithfully repeat an outdated or incorrect document
    • Faithful answers are always too short
  7. Why should teams pin the judge model and settings when comparing RAG systems?
    • Pinning makes the judge free to run
    • Judges only work with one vector database
    • Pinning removes the need for test questions
    • LLM-judge scores vary with the judge model and prompt, so comparisons need identical settings
  8. Retrieval returns the right passages, but the answer adds unsupported details. Where should the team look first?
    • The chunking strategy
    • The prompt or the generation model
    • The embedding model
    • The number of documents indexed