DirectiveAI®
Start With Prompting — Almost Always
Prompting is fast, cheap to iterate on, and easy to change when requirements shift. For most tasks, a well-structured prompt with good examples and retrieval gets you further than teams expect. It should be the default, not the fallback.
The mistake is reaching for fine-tuning to fix a problem that is really a prompting or context problem in disguise.
What Fine-Tuning Actually Buys You
Fine-tuning shines when you need consistent behavior on a narrow task at scale: a fixed output format, a specific tone, a classification the base model keeps getting subtly wrong, or latency and cost savings from a smaller model doing one job well.
It does not reliably add new knowledge, and it will not rescue a task that is poorly defined. If you cannot describe success precisely enough to build an evaluation set, you are not ready to fine-tune.
The Real Cost Is the Data
The compute for fine-tuning is rarely the expensive part. The expensive part is assembling and maintaining a high-quality dataset — and keeping it current as the task drifts. A fine-tuned model is a frozen snapshot of your data; when the world changes, it does not.
Prompting, by contrast, keeps you flexible. You can adjust behavior in an afternoon instead of a retraining cycle.
A Practical Decision Rule
Prompt first. Measure. If the failures are consistent, well-defined, and high-volume — and prompting has plateaued — fine-tune to lock in the behavior and cut cost. If the failures are varied or the task is still evolving, keep iterating on context and retrieval.
Why It Matters
The choice is not about which technique is more advanced. It is about matching the tool to the shape of the problem: prompting for flexibility and speed, fine-tuning for consistency and scale. The teams that ship well know which problem they actually have.
