Traditional AI and machine learning is essentially pattern recognition at scale. ML can be used in various predictive settings, but it typically requires customized training and rarely applies to anything it hasn’t been specifically trained on.

GenAI builds on traditional ML – you can do a lot of the same things, like predictions based on data – but it doesn’t usually need specific training on your data set, and it adds the ability to create new digital assets upon request. This applies to all sorts of asset types – text, images, videos, code – and can be applied in some way in almost any business setting.

Learning to use GenAI and to leverage it for your business requires a focused investment of time and effort.  Even if the technology evolves and improves, the investment in figuring out how it might work in your environment is worth making now.  And if you do find a way to drive ROI, the benefits of that optimization will start to compound as soon as you get started.  You also don’t want to lag your competitors, who may figure out how to optimize their workflows and products before you do.

Generally speaking, no. The only people who need a custom hosted or fine-tuned LLM are either working with highly unusual data sets, have cost considerations that make cloud solutions too expensive, or security concerns that take cloud off the table. For most internal use cases (knowledge bases, code generation, content creation) off-the-shelf LLMs are going to be fine.

As noted above, costs can be an issue, but typically only if security considerations require you to host your own model or if you are running at consumer scale.  We can help predict costs by calculating expected usage over time, and match those costs to technical solution requirements.

The best thing about LLMs is that they will improve on their own!  Stride can help you balance expected model evolution with pure infrastructure investments that will apply to any LLM-powered application stack.  Of course you don’t want to overbuild your plumbing, but good architecture makes it easier to leverage the power of LLMs.