Quiz połączony z przewodnikiem · Średni Poziom

Unikanie uzależnienia od dostawców sztucznej inteligencji dzięki quizowi dotyczącemu strategii obejmującej wiele modeli

Testuje warstwy blokowania LLM, dlaczego przełączanie modeli osadzania wymusza ponowne indeksowanie oraz w jaki sposób zestawy ewaluacyjne i routing kanarkowy zapewniają bezpieczeństwo przełączania.

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Pytanie 1 z 8

Why does switching to a new embedding model usually mean re-embedding the whole corpus?

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Odpowiedzi i wyjaśnienia pojawiają się w miarę rozwiązywania quizu.

  1. Why does switching to a new embedding model usually mean re-embedding the whole corpus?
    • New models refuse to read old databases
    • Vector databases delete vectors after a model change
    • Vectors from different embedding models are in different spaces and cannot be compared
    • Embeddings expire after a fixed number of days
  2. What is the main purpose of an evaluation suite in a multi-model strategy?
    • To measure whether a candidate model meets quality standards on your real tasks before switching
    • To get a lower price from the current vendor
    • To encrypt prompts before they are sent
    • To train a new model from scratch
  3. Why does putting every model behind one unified API not guarantee portability?
    • Unified APIs only work with open-source models
    • Unified APIs are always slower than direct calls
    • Unified APIs do not support streaming
    • Prompts tuned to one model's behavior often perform differently on another
  4. Sending simple tagging tasks to a small model and complex complaints to a frontier model is an example of what?
    • Dostrajanie
    • Routing tasks by difficulty to control cost
    • Zero data retention
    • Re-embedding
  5. What should be stored with every vector to make future re-indexing straightforward?
    • The user's password
    • The billing account number
    • The embedding model's name and version, along with source text and chunking settings
    • Only the vector dimensions
  6. Which of these is an example of platform-feature lock-in?
    • Relying on a provider's hosted conversation threads and built-in vector store
    • Sending requests in JSON
    • Using HTTPS for API calls
    • Logging response latency
  7. What is usually true of fine-tuned models on hosted provider platforms?
    • They can be downloaded and run anywhere
    • They automatically work with every other provider
    • They cost nothing to keep
    • They usually cannot be exported to another provider
  8. What does canary routing mean when adopting a new model?
    • Switching all traffic at once overnight
    • Sending a small share of traffic to the new model and comparing results before widening
    • Testing only with synthetic data
    • Letting users choose their model manually