When it comes to GenAI in the enterprise, excitement is colliding with reality. Leaders recognize the technology's power and eagerly want to unleash it on their own operations. But many are frustrated by ongoing performance issues.
Companies are realizing that large, general-purpose models designed to summarize the vast Internet archives fall short when expected to deliver tailored insights about their businesses. Now, the focus is on using these increasingly powerful systems as a foundation for customized solutions designed to help drive a competitive advantage for the business.
While this path from foundational to tailored LLMs will look different for every company, each will require new tooling to help their developers deliver the accurate and governed GenAI that leaders are demanding.
For most businesses, the GenAI journey starts off by experimenting with large, foundational models. These could be proprietary models or, increasingly, open source systems.
In this nascent stage, leaders must consider the official policies that will guide usage. Then, they begin to allow internal teams to test different systems. This helps to determine the technical and organizational hurdles that must be addressed and hone in on the early use cases that promise to deliver the most value.
Soon enough, the focus becomes overcoming the performance limitations that prevent broader use of AI across the business. Organizations want to learn how to customize LLMs to their specific needs. This typically involves using a technique like retrieval augmented generation, along with a company's own private corpus of data, to further train the models to respond to unique questions about the business.
Eventually, companies will want to drive even more customization and exert greater control over the outputs. This requires deeper, fine-tuning of the models. For enterprises with massive datasets, they may even seek to pretrain their own LLM.
Learn more here about the typical path to customized GenAI.
Each of these stages presents its own set of organizational and technical hurdles. With GenAI advancing so quickly, companies must be nimble in their approach to building and supporting the technology. A unified but flexible foundation is key, one that can drive the data quality and strong governance needed for trustworthy and performative AI.
Data preparation, retrieval models, language models, ranking and post-processing pipelines, prompt engineering, etc.; there are many components involved in building a GenAI system. Developers need an underlying platform that combines and optimizes all aspects of this process, as well as provides rich tools for understanding the quality of their data and model outputs.
With Databricks' Data Intelligence Platform, engineers have access to a broad set of services designed to help address the distinct challenges across every step of the GenAI maturity cycle. Solutions exist to help organizations experiment with models, train and fine-tune systems across thousands of GPUs and incorporate human feedback for quality and safety improvement.
From one foundation, businesses can manage the lifecycle of all their AI systems, driving outputs that are highly accurate, governed and safe. With the DI Platform, companies are limited only by their imagination and willingness to experiment. For more information, check out the "Big Book of GenAI."
