AIF-C01 Fundamentals of Generative AI Practice Question

A company is building a chatbot that must provide accurate answers based on internal documents without retraining the model. Which approach should they use?

⚠ Common exam trap

Test-takers frequently confuse fine-tuning (which requires retraining) with RAG (which does not), or mistakenly think RLHF or distillation can inject new factual knowledge without retraining, when in fact they address alignment, efficiency, or behavior, not dynamic knowledge retrieval.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Prompt engineering with retrieval-augmented generation (RAG)

Retrieval-augmented generation (RAG) allows the chatbot to fetch relevant internal documents at inference time and incorporate them into the prompt, providing accurate, up-to-date answers without retraining the model. This approach combines prompt engineering with a retrieval step, ensuring the model's responses are grounded in the company's specific knowledge base while keeping the base model frozen.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Reinforcement learning from human feedback (RLHF)
Why it's wrong here
RLHF aligns model behavior but does not inject new knowledge.
✗
Fine-tuning the model on internal documents
Why it's wrong here
Fine-tuning requires retraining and may be costly and not dynamic.
✗
Model distillation to a smaller model
Why it's wrong here
Distillation reduces model size but does not incorporate new documents.
✓
Prompt engineering with retrieval-augmented generation (RAG)
Why this is correct
RAG retrieves relevant documents at inference time, providing up-to-date answers.

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AIF-C01 Fundamentals of Generative AI Practice Question

Why each option matters

Option-by-option breakdown

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