Organizations are rapidly expanding their use of generative AI across all parts of the business. Applications requiring deep domain expertise or specific business context need models that truly understand their proprietary knowledge, workflows, and unique requirements.
While techniques like prompt engineering and Retrieval Augmented Generation (RAG) work well for many use cases, they have fundamental limitations when it comes to embedding specialized knowledge into a model’s core understanding. Supervised fine-tuning and reinforcement learning help in customizing the model, but they operate too late in the development lifecycle, layering modifications on top of models that are a fully trained, and therefore difficult to steer to specific domains of interest.
When organizations attempt deeper customization…
