Your company may be using generative AI to analyze vast datasets, shape business strategies, streamline interactions, and provide personalized responses to customers. So you may be at the point where you’re asking a critical question about your tech stack: “Should we train our own large language model (LLM) or license a pre-trained LLM?” It’s an important question and, for the majority of companies, taking the pre-trained LLM path is often the smarter choice.
Sure, it sounds appealing to have something completely bespoke from the start, but in reality, LLM training is a large undertaking that might not be worth the time, money, and effort. Between steep costs, legal approvals, difficulty scaling and updating, and a hefty environmental impact, there are a few downsides to consider.
Many leaders don’t realize you can customize an LLM without having to fully train it from the ground up. A pre-trained model (also known as
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