This Week's Term: Few-shot prompting - the technique of providing AI with 2-5 examples of desired output before asking it to generate new content, enabling the model to learn the pattern and apply it to similar tasks.
Remember learning to write proposals to customers or feedback to employees in the annual review? Your manager or instructor probably didn't give you a 10-page style guide. They showed you a few good examples and said "write something like this." Few-shot prompting works the same way.
Instead of describing what you want in abstract terms, you show AI concrete examples. If you need customer support responses in your brand voice, include 2-3 actual responses your team has sent. If you're drafting marketing content, provide samples that match your tone and structure. The AI learns from demonstration, not just instruction.
The business impact is significant. Digital marketing agencies report that few-shot prompting solves one of their biggest AI frustrations: generic content that requires extensive editing. By including examples of past work, they get AI output that matches client voice without needing to fine-tune custom models for each client. E-commerce companies use it to categorize hundreds of products weekly by showing a few categorization examples, turning a manual bottleneck into an automated workflow.
Research shows that quality beats quantity. Two perfect examples deliver more value than ten mediocre ones. And there's a plateau effect - most gains come from the first 2-3 examples. Beyond five examples, you rarely see additional improvement.
This connects directly to what we explored in the leadership principle section. When we discussed exercising the "Are Right, A Lot" principle, we asked how you teach AI to judge like you do. Few-shot prompting is one practical answer: you show AI examples of good judgment calls, and it learns to apply similar judgment to new situations. You're not writing algorithms or training models. You're teaching through demonstration.
The same pattern appears in GloryHack's approach. Instead of asking AI to be randomly creative, it uses 10 proven creative templates with "few-shot" examples for each - essentially encoding decades of expertise into examples that AI can adapt to specific situations.
Your practical takeaway: Next time AI output sounds generic or misses the mark, don't immediately think "we need a custom model" or accept mediocre results. Try adding 2-3 examples of exactly what you want. You'll often find that's all it takes to get output that matches your needs.
To learn more about few-shot prompting techniques, watch the Google AI Essentials video below: