CAIC Natural Language Processing & AI Applications Flashcards

What is the purpose of fine-tuning a pre-trained language model?

Answer: To adapt the model to a specific domain or task using additional training data

Fine-tuning updates a pre-trained model's weights on domain-specific data, allowing it to perform better on specialized tasks without training from scratch.

Which AI application would a CAIC consultant most likely recommend for automating customer support ticket routing?

Answer: Text classification with NLP

Text classification models can automatically categorize incoming support tickets by topic, urgency, or department, enabling intelligent routing without human intervention.

What does 'zero-shot prompting' mean when working with large language models?

Answer: Asking the model to perform a task without any examples in the prompt

Zero-shot prompting asks the model to complete a task based on instructions alone, without providing any input-output examples to demonstrate the desired behavior.

In conversational AI design, what is a 'fallback intent'?

Answer: A response triggered when the chatbot cannot match user input to any known intent

A fallback intent is triggered when the NLP engine cannot confidently classify the user's input, typically prompting a clarification question or graceful error message.

Which metric best evaluates how well a chatbot resolves user queries without human escalation?

Answer: Containment rate

Containment rate measures the percentage of conversations fully handled by the bot without escalating to a human agent, reflecting self-service effectiveness.

What is the key advantage of using a pre-trained foundation model over building an NLP model from scratch for an enterprise deployment?

Answer: They require significantly less labeled training data and development time

Foundation models encode broad linguistic knowledge from massive pre-training, so organizations can adapt them with minimal task-specific labeled data, saving significant time and cost.