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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.
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