Part 7/11:
OpenAI’s approach reflects this reality—current models aim to pattern-match rather than know. They do not have a built-in theory of mind or self-awareness regarding their knowledge gaps.
Is Fine-Tuning Worth It?
Despite its limitations, fine-tuning remains valuable for teaching patterns, formats, and specific tasks. For example, training a model to generate long-form fiction or specific code snippets.
But:
It is not suitable for creating an institutional knowledge base.
It requires significant effort and expertise.
It may still produce hallucinations and inaccuracies.
David emphasizes that prompt engineering generally outperforms fine-tuning for many user needs—it's less costly, faster, and more scalable.