updatE

Author: mrbullwinkle

articles/cognitive-services/openai/concepts/prompt-engineering.md

@@ -78,7 +78,7 @@ Successful prompts often rely on the practice of “one-shot” or “few-shot
 | Learning Type|  Prompt| Completion|
 |------------------------------------- |-------|-----|
 | Zero-shot | Headline: Coach confident injury won't derail Warriors<br>Topic:| The coach is confident that the injury won't derail the Warriors' season. The team is still focused on their goals and that they will continue to work hard to achieve them.|
-| One-shot | Headline: Twins' Correa to use opt-out, test free agency<br>Topic: Baseball<br>Headline: Qatar World Cup to have zones for sobering up<br>Topic: Soccer<br>Headline: Yates: Fantasy football intel for Week 6<br>Topic: Football<br>Headline: Coach confident injury won't derail Warriors<br>Topic: | Basketball |
+| Few-shot | Headline: Twins' Correa to use opt-out, test free agency<br>Topic: Baseball<br>Headline: Qatar World Cup to have zones for sobering up<br>Topic: Soccer<br>Headline: Yates: Fantasy football intel for Week 6<br>Topic: Football<br>Headline: Coach confident injury won't derail Warriors<br>Topic: | Basketball |
 
 The example above illustrates the utility of few-shot learning. Without the examples, the model seems to be guessing at the desired behavior, while the examples cleanly show the model how to operate. This also demonstrates the power of the model, it can infer the category of label that is wanted, even without a ‘basketball’ label in the examples.