Dis correct — this example follows thegold standard for few-shot prompting, as defined in UiPath’s Prompt Engineering methodology. The format usesclearly labeled input-output pairs, giving the agent:
Consistent structure to follow
Explicit tone classification
Variety across sentiment categories
Each example models the task exactly as it should be performed:
Input: [Text]
Output: [Label] (Positive, Neutral, Negative)
This design teaches the agenthow to recognize patterns in user tone, even with subtle expressions. It works especially well in LLM-powered agents that handlefeedback analysis,review classification, orcustomer support automation.
Option A (listing keywords) lacks structure and will not generalize well.
B is incomplete — there’s no output for the model to learn from.
C uses a rating scale, which doesn’t match the classification labels needed.
UiPath emphasizes thatwell-structured few-shot examplesimprove LLM accuracy dramatically — especially when working with ambiguous or emotionally nuanced language.
This approach improvessentiment classification precision, reduces hallucination, and ensures consistent labeling across varied input phrasing — making the agent more reliable in real-world scenarios.
