LLM Bottlenecks

I’ve been talking about Generative AI and LLMs a recently here.

There are dozens of use cases that are exciting and with tangible business value. So what is stopping everybody from adopting these technologies across their business?

Two key bottlenecks:

• Data quality and data management

If you need to build an LLM on your proprietary data, then the quality and management of your data is paramount. LLMs are already known to hallucinate. Giving the model bad data is only going to create more variability in the quality of responses you get.

• Computing costs

These models are very compute intensive which is costly. The larger the model, or the heavier it is used, can drive up the costs significantly.

Computing costs will come down with time - as the hardware becomes cheaper and the models become more optimized.

But data quality and management only gets worse with time.

So while you wait for computing costs to drop, spend your time building data quality standards and management best practices.

That way, when your budget is ready for LLMs

Your data will be as well,

Sawyer