A Generative Al Engineer is creating an LLM-based application.

When prioritizing cost and latency over quality in a Large Language Model (LLM)-based application, it is crucial to select a configuration that minimizes both computational resources and latency while still providing reasonable performance. Here's whyDis the best choice:

Context length: The context length of 512 tokens aligns with the chunk size used for the documents (maximum of 512 tokens per chunk). This is sufficient for capturing the needed information and generating responses without unnecessary overhead.

Smallest model size: The model with a size of 0.13GB is significantly smaller than the other options. This small footprint ensures faster inference times and lower memory usage, which directly reduces both latency and cost.

Embedding dimension: While the embedding dimension of 384 is smaller than the other options, it is still adequate for tasks where cost and speed are more important than precision and depth of understanding.

This setup achieves the desired balance between cost-efficiency and reasonable performance in a latency-sensitive, cost-conscious application.