FAQS
What is a foundation model and why does selecting the right one matter?
A foundation model is a large AI model trained on broad data that can be adapted for specific tasks. Examples include GPT-4, Claude, Llama, and Mistral. Choosing the right model affects your product's performance, cost, latency, data privacy, and long-term scalability.
What criteria should I use to evaluate foundation models for my use case?
Key criteria include accuracy on your specific tasks, context window size, cost per token, latency, available fine-tuning options, data privacy terms, licensing, and the vendor's reliability and roadmap. A specialist can run benchmark tests on your actual data to compare models objectively.
Can I switch foundation models after my product is built?
In principle yes, but switching models mid-project can require significant re-engineering of prompts, workflows, and integrations. Making the right model choice upfront with professional guidance is far more efficient.
Key criteria include accuracy on your specific tasks, context window size, cost per token, latency, available fine-tuning options, data privacy terms, licensing, and the vendor's reliability and roadmap. A specialist can run benchmark tests on your actual data to compare models objectively.
In principle yes, but switching models mid-project can require significant re-engineering of prompts, workflows, and integrations. Making the right model choice upfront with professional guidance is far more efficient.
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