The lack of linguistic diversity in NLP is a significant problem, with most resources devoted to English language models, leaving other languages underrepresented and biased. This poses challenges for conversational applications and models that must adapt to diverse populations. To address this, researchers have been exploring multilingual language models (MLLMs), which can handle multiple languages and improve machine translation performance between resource-rich languages. MLLMs come in different architectures, such as encoder-only, decoder-only, and encoder-decoder models, and are typically pre-trained across data from multiple languages. Effective prompting strategies, such as monolingual prompting, translate-test prompting, cross-lingual prompting, chain-of-thought prompting, and aggregation, can improve MLLM performance. However, tokenization issues and limited linguistic diversity in training data lead to inconsistencies in model performance across languages. The Nebula family of models from Symbl.ai addresses these challenges by providing out-of-the-box support for longer context windows and multiple languages, allowing for more accurate and nuanced conversational AI.