2 posts tagged with "capability-elicitation"

Most teams debugging a bad LLM output reach for the same fix: rewrite the prompt. Add more instructions. Clarify the format. Maybe throw in a few examples. This is prompt engineering in its most familiar form — making instructions clearer so the model understands what you want.

But there's a different failure mode that better instructions can't fix. Sometimes the model has the knowledge and can perform the reasoning, but your prompt doesn't activate it. The model isn't confused about your instructions — it's failing to retrieve and apply capabilities it demonstrably possesses.

This is the domain of capability elicitation. Understanding the difference between "the model can't do this" and "my prompt doesn't trigger it" will change how you debug every AI system you build.

Most prompt engineering advice focuses on the wrong problem. Teams spend weeks refining instruction clarity — adding examples, adjusting tone, restructuring formats — when the actual bottleneck is that the model fails to activate knowledge it demonstrably possesses. The distinction matters: prompt engineering tells a model what to do, while capability elicitation gets a model to use what it already knows.

This isn't a semantic quibble. The UK's AI Safety Institute found that proper elicitation techniques can improve model performance by an amount equivalent to increasing training compute by five to twenty times. That's not a marginal gain from better wording. That's an entire capability tier sitting dormant inside models you're already paying for.