Are We Building AI Applications on Models That Don't Understand Our Domain?

Most developers today are building applications using LLM APIs. We connect a model, add a system prompt, integrate RAG, and build an application around it.

But I think there is a bigger question we should be discussing.

Does the model actually understand the domain in which we are using it?

Consider a few examples:

• A fintech application needs to understand credit policies, underwriting rules and financial terminology.
• A legal AI application needs to interpret clauses, contracts and exceptions.
• A healthcare application needs to understand clinical terminology and domain-specific decision rules.

Adding documents through RAG can help provide context. Fine-tuning can help adapt model behaviour. But building reliable domain-specific AI involves more than simply connecting documents or changing prompts.

I believe we need to think about Domain-Aligned AI.

Instead of treating the model as a generic intelligence and adding more layers around it, what if we could align the model itself with the knowledge, behaviour and requirements of a specific domain?

This is one of the problems we are exploring at Nugen Intelligence.

We are working on ways to help developers build, align, evaluate and deploy AI models using their own domain data.

But I would love to hear from developers working on real-world AI applications.