LLM’s for Enterprises: Why Bigger isn't Always Better?

To drive optimal results on your private data go Deeper than Bigger. Experimentation shows that you can get better results with LLM’s 100x smaller.

By this point, you must have attempted to utilize LLM with your private enterprise data. After initially obtaining intriguing results by assembling boxes and engaging in API-level engineering for (as illustrated in the previous blog about RAG- Retrieval Augmented Generation), the harsh reality sets in that achieving an acceptable level of accuracy with your enterprise data is far more complex than initially perceived.

To optimize your generative AI output, one must concentrate on data and feature engineering, fine-tuning, instructing the database, implementing a human feedback mechanism, and establishing a reward model. These steps necessitate a solid understanding of Data Science fundamentals, and I will address each of them individually. However, before delving into those topics, we must dispel a major misconception. It seems that everyone is fixated on the size of the LLM. However, for an enterprise AI team, the model’s size is an incorrect aspect to focus on. My team has conducted extensive experimentation and research on this matter, and we will soon publish a paper outlining our detailed findings. In summary, what we have learned is that emphasizing the parameter size of the models is misguided when identifying the elements that should truly…