The most effective way to prepare for change is to expect it. When selecting software, whether from a vendor or as part of internal development, consider whether it’s designed to support swapping out one model for another. This applies to all software, but as the pace of change is so rapid in AI, it’s especially true for software that relies on LLMs.
If you need to change models to keep up with your peers, then you’ll want to be able to do so quickly and easily. Regardless of whether your primary plan is to use GPT-4 or an open-source model like Llama2, you must have a backup plan.
The best insurance policy is to avoid embedding lock-in in the first place. This is why we’ve not only designed Kelvin to support open embeddings but also chosen to provide customers with traditional escrow and source code access to the Kelvin software and our proprietary models. Even if we were to go out of business, our customers would still be able to use our embedding models to support their existing workflows.
It’s no mistake that we designed Kelvin to be modular, supporting the use of different components across the entire stack – most especially LLMs. This modularity has a cost, as it requires us to support multiple interfaces and implementations, but it also provides customers with the flexibility and peace of mind that no one component will be a single point of failure.
Source: Kelvin Legal
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