A developer is implementing a few-shot structured prompt for an email classification task. The prompt includes examples of email subjects labeled with their respective classifications, such as "Spam" or "Work".What is the most important aspect to consider when selecting examples for the prompt?
Answer(s): C
In few-shot prompting, the quality of examples is crucial. They must be diverse, relevant, and representative of the real inputs the agent will process, ensuring the model generalizes correctly to the classification task.
An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:
Grouping evaluations into sets (valid web results Slack notifications, invalid web results escalations, and edge cases) ensures all workflows are covered without redundant tests. This structured approach provides comprehensive coverage while maintaining efficiency.
What are the primary benefits of Context Grounding when querying data across multiple documents?
Answer(s): D
Context Grounding allows the agent to understand relationships between data points across multiple documents, which enables advanced tasks such as summarization, comparison, and retrieving highly relevant insights with contextual accuracy.
An agent is built to extract customer feedback sentiment. You want to show the LLM how to classify it as `Positive', `Neutral', or `Negative'.Which few-shot design is most helpful?
Answer(s): B
Providing clear few-shot examples that map full customer feedback texts directly to the sentiment labels ("Positive", "Neutral", "Negative") guides the LLM to consistently produce the correct classification format.
Which configuration area defines what the agent should do after a human resolves the escalation?
The Outcome behavior section specifies the agent's next action after a human resolves the escalation, defining how the process continues.
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