Opinion Corner

Here I share my thoughts on various topics related to technology and education. Please note the following, especially if you disagree:
  • The opinions expressed here are my own and do not represent those of my employer or any other organization I am affiliated with.
  • Given the venue and space constraints, these posts are brief and may not fully capture the complexity of the topics discussed.
  • These posts are meant to stimulate thought and discussion; they may not reflect my most current views as I continue to learn.

I am discussing insights from a unique experience working with an innovative startup, PredictAP, that leverages AI to solve hard problems in the real-estate management domain. This collaboration began in the summer of 2021 with AI Jumpstart, a program initiated by visionaries in the Massachusetts government and at Northeastern University with the goal of bringing together AI-focused small Massachusetts businesses and faculty experts to create synergies and foster innovation. Since then, I have been deeply involved in this project, which included spending an entire year during my sabbatical as a member of the engineering team.

Context Matters: Why Domain-Specific AI Outperforms Foundation Models (March 31, 2026)

TL;DR

Foundation models (such as LLMs) bring broad language understanding and world knowledge but lack domain expertise, while traditional supervised learning can master a specific task but misses the bigger picture. Domain-specific AI combines both: it starts with a foundation model and adapts it to a particular use case—such as automated invoice coding—using techniques like prompt engineering, RAG, fine-tuning, distillation, or agentic architectures. For complex real-world problems that require both general knowledge and specialized expertise, domain-specific AI doesn't compete with foundation models, but it builds on them.

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