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What is the primary purpose of a system message in a prompt?
To define the model's role, behavior, and output constraints.
To provide training data that permanently changes the model.
To retrieve data from an external data source.
When should you use Retrieval Augmented Generation (RAG) instead of relying on prompt engineering alone?
When you want the model to respond in a consistent style and format.
When the model needs access to domain-specific or current data that it wasn't trained on.
When you want to reduce the length of prompts sent to the model.
What does the temperature parameter control in a language model?
The maximum number of tokens the model can generate.
The randomness and creativity of the model's responses.
The speed at which the model processes requests.
What does fine-tuning optimize in a language model?
The factual accuracy of responses by connecting to external data.
The consistency of the model's behavior, style, and output format.
The number of tokens the model can process in a single request.
You're building a chat application that needs to answer questions using your company's product catalog while maintaining a specific brand voice. Which combination of strategies is most appropriate?
Prompt engineering only, with detailed system messages.
RAG for the product catalog data, fine-tuning for the brand voice, and prompt engineering for conversation-specific instructions.
Fine-tuning only, with the product catalog included in the training data.
You must answer all questions before checking your work.
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