Stop Confusing Data Contextualization with AI Model Training

There's a lot of panic lately about companies like Salesforce and Slack restricting how you use your data with AI. But most reactions miss the core point: there's a huge difference between embedding your data directly into AI models (fine-tuning) and just giving AI temporary access to your data when you ask a question (contextualization).

Think of fine-tuning as permanently baking your ingredients into the cake. Once they're mixed and baked, you can't remove them. Privacy, ownership, and trust concerns are legitimate here because your data is now part of a larger, shared model.

Contextualization methods, like FAISS and RAG, are more like dipping fries into ketchup. Your fries (data) stay separate from the ketchup (AI model). They only come together briefly when you're ready to eat (query). Your data stays yours, private, controlled, and easily managed.

Why does this matter? Contextualization isn't just safer, it's smarter. You're customizing AI to your business without permanently surrendering your proprietary insights to a giant model trained on the entire internet.

The confusion around this is unnecessary noise. Enterprise AI done right involves contextualizing your data, not blindly mixing it into external models. So, next time you see someone panicking about Salesforce "locking down data," remember the fries and ketchup.

Want to dive deeper or set up contextualized AI for your business? Reach out and let's make AI work smarter for you.