Dis correct — includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.

UiPath encourages the use of examples for:

Classification(e.g., labeling sentiment, email categories)

Transformation tasks(e.g., turning unstructured text into tables)

Step-by-step instructions(e.g., troubleshooting flows)

These examples serve two purposes:

Pattern induction: The model picks up on consistent structures or rules used across examples.

Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.

Best practice:

Usetypical, real-world examplesrepresentative of the data the agent will encounter.

Keep formatsclear and consistentacross input-output pairs.

Pair examples withexplicit instructionsin the system or user prompt.

Option A is flawed — focusing only on edge cases can confuse the model.

B is false — omitting examples forces the LLM to guess the structure, reducing accuracy.

C is misleading — examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.

In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.