There is a long list of Large Language Models (LLMs) out in the wild already, from OpenAI’s GPT-4 to Google’s PaLM2 to Meta’s LLaMA, to name three of the more high profile examples. Differentiation between LLMs is determined by factors including the core architecture of the model, training data used, model weights applied and any fine tuning for specific contexts/purposes, as well as the cost of development (and the relative budget of the model maker to splurge on those costs) — all of which can influence how this flavor of generative AI performs in response to a user’s natural language...