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Frequently Asked Questions
Paul Zabelin edited this page Mar 27, 2025
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- AI community dominant and friendly
- AI API Service We extract as small as possible AI Service, that would handle model management code, retries, error handling. Any other language can invoke API via https. Example: NodeJS app calls AI API to get a response to show in a chat app.
- Because we need to store results of the test runs over iterations. So we can see our progress of improving the reliability of an AI application under test.
- No, even technically yes. Shorter embeddings help to have smaller snapshots to cache embedding responses. Embeddings are only used in tests to compare similarities to correct and incorrect responses.
When using a smaller number of dimensions in embeddings, you may observe more stable values for the same input due to several factors:
- Reduced Complexity: Lower-dimensional embeddings capture fewer features, leading to simpler models that are less sensitive to variations in the input data. This reduction in complexity can result in more consistent and stable embeddings.
- Enhanced Generalization: With fewer dimensions, embeddings are less likely to overfit to noise or specific patterns in the training data. This improved generalization means that the embeddings remain more stable across different inputs.
- Improved Alignment: Higher-dimensional embeddings can suffer from misalignment issues, where small changes in input lead to significant variations in the embedding space. Lower-dimensional embeddings tend to have better alignment, contributing to their stability.
It’s important to note that while reducing the number of dimensions can enhance stability, it may also limit the embedding’s ability to capture complex relationships within the data. Therefore, it’s crucial to balance dimensionality with the specific requirements of your application to achieve optimal performance. See Alignment and Stability of Embeddings and ChatGPT