AI Model Families: Pick an Embedding Model You Can Live With

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-model-families-embeddings-pick-r8a1-creators

1. What makes switching embedding models particularly expensive for large document collections?

   1. The API keys must be re-registered with each provider
   2. Re-embedding millions of documents requires significant computational resources
   3. The new model must be fine-tuned on all existing data
   4. Query latency increases temporarily during the transition

2. Why is recall@k preferred over precision for evaluating embedding model retrieval quality?

   1. Precision requires human-labeled ground truth but recall@k does not
   2. Recall@k is faster to compute than precision metrics
   3. Embedding models are optimized for recall by design
   4. Recall@k measures how many relevant documents are retrieved out of all relevant documents available

3. What information should be stored alongside each embedding to minimize future switching costs?

   1. The exact hyperparameters used during training
   2. The timestamp when embedding was generated
   3. The model version identifier
   4. The original source text or document content

4. Which of the following is a task that AI cannot reliably assist with when selecting an embedding model?

   1. Predicting whether a provider will deprecate or change pricing for their model
   2. Recommending dimension and quantization tradeoffs
   3. Designing a retrieval evaluation test using your actual queries
   4. Evaluating recall@k performance on your specific document corpus

5. What does hybrid retrieval combine that pure embedding-based retrieval lacks?

   1. Vector databases with graph databases
   2. Dense and sparse embedding representations
   3. Keyword-based or exact matching with semantic similarity
   4. Multiple embedding models for redundancy

6. When evaluating embedding model candidates, what should be the primary selection criterion?

   1. Retrieval quality measured on your specific queries and corpus
   2. The model's popularity ranking on provider websites
   3. The model's context window size
   4. The number of dimensions the model outputs

7. What is a key trade-off when choosing embedding model dimensions?

   1. Higher dimensions increase API costs linearly
   2. Dimension choice affects only indexing speed, not retrieval quality
   3. Higher dimensions always improve retrieval quality
   4. Lower dimensions reduce storage but may lose semantic distinction

8. What does quantization do to embedding models?

   1. It improves recall by adding more training data
   2. It increases the context window of the model
   3. It reduces the number of dimensions in the embedding
   4. It converts floating-point vectors to lower-precision representations

9. Why is it important to describe your corpus and query types when selecting an embedding model?

   1. To verify the model is available in your region
   2. Because different models perform differently across domains and query patterns
   3. To calculate the exact API costs in advance
   4. To determine if the model supports your language

10. What is the recommended size for an initial retrieval-quality evaluation set?

    1. Exactly 100 queries as mentioned in the lesson
    2. As many queries as you can afford to annotate
    3. At least 10 queries to get statistically significant results
    4. A minimum of 1000 queries for production readiness

11. What happens if you store embeddings without the original source text?

    1. Storage costs are reduced by approximately 50%
    2. Switching to a new embedding model becomes a data recovery problem, not just compute
    3. The embeddings become more accurate over time
    4. Retrieval speed improves significantly

12. Which statement about provider stability is correct?

    1. Provider stability should be considered when selecting models
    2. Major providers never deprecate embedding models
    3. All embedding providers offer the same uptime guarantees
    4. You should always choose the cheapest provider

13. What does 're-embed cost' refer to in the context of embedding model selection?

    1. The price difference between API providers
    2. The cost to initially train an embedding model from scratch
    3. The cost of storing embeddings in memory
    4. The computational expense of processing all documents with a new model

14. Why might an embedding model perform well on benchmarks but poorly on your specific retrieval task?

    1. The model is likely overfitting to benchmark data
    2. Benchmarks are always more rigorous than real-world tasks
    3. Benchmark evaluations use different similarity metrics
    4. Your documents may use domain-specific terminology or structures not well-represented in benchmark data

15. What is the premise of the lesson on embedding model selection?

    1. Embedding models are interchangeable and any will work equally well
    2. Embedding models should be changed frequently to stay current
    3. Providers offer free switching between their embedding models
    4. Once embedded, switching models is costly, so choose based on retrieval quality for your data