The age of large language models and generative AI has sparked excitement for business leaders. But those who want to launch their own LLM face many hurdles between wanting a production generative AI tool and developing one that delivers real business value and sustained advantage.
Private data is a moat—a potential competitive advantage. By leveraging your proprietary data and subject matter expertise, you can build generative models that work better for your domain, your chosen tasks and your customers.Retrieval augmented generation, better known as RAG, allows your generative AI pipeline to enrich prompts with query-specific knowledge from a company’s proprietary databases or document archives.
If an organization feels that it needs a model custom-built from the ground up, its data team first selects a model architecture and then trains it on unstructured text—initially on a large, generalized corpus, then on proprietary data. This teaches the model to understand the relationships between words in a way that’s specific to the company’s domain, history, positioning and products.
Even retrieval augmentation benefits from data labeling. Although vector databases efficiently handle relevance metrics, they won’t know if a retrieved document is accurate and up to date. No company wants its internal chatbot to return out-of-date prices or recommend discontinued products.Using your proprietary data to build your AI moat requires work, and that work rests heavily on data-centric approaches including data labeling and curation.