Perspectives

Questions I am genuinely curious about.

Thoughts, frameworks, and questions that shape my understanding of Applied AI, from why some AI initiatives succeed while others fail to how organizations can adopt AI more intentionally.

Retrieval before reasoning

The instinct with language models is to make them smarter. Usually the bigger lever is making sure they're looking at the right thing in the first place. Why grounding, not raw intelligence, is what makes AI trustworthy enough for real work.

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In progress

Questions taking shape next:

  • Why do some AI projects succeed while others fail?
  • How should businesses evaluate AI opportunities?
  • When is AI the wrong solution?
  • What architectural decisions influence long-term success?