Best practices

Selecting the most appropriate embedding models and vector size is not merely a technical decision, but a strategic one that aligns with the unique characteristics, technical and organizational constraints, and objectives of your project.

Maintaining computational efficiency and cost is another cornerstone of effectively using embedding models. As some models can be resource-intensive and have higher response times and higher cost, optimizing the computational aspects without sacrificing the quality of the output is essential. Designing your system to use different embedding models depending on the task at hand will yield a more resilient application architecture.

It’s imperative to regularly evaluate your embedding model to ensure your AI/ML application continues to perform as expected. This involves routinely checking performance metrics and making necessary adjustments. Tweaking your model usage could mean altering vector sizes to avoid overfitting—...