Fine-Tuning vs RAG vs Prompting Quiz

Answers and explanations appear as you take the quiz.

1. A model answers questions about your company's pricing incorrectly because prices change every quarter. Which approach best fits this gap?
   • Fine-tune the model on last quarter's price list
   • Use RAG to retrieve the current price documents at answer time
   • Lower the model's temperature
   • Add more examples of polite answers to the prompt
2. According to the guide, what is usually the first lever to try for format and style problems?
   • Full fine-tuning
   • Building a vector database
   • Prompting with clear instructions and examples
   • Switching to a larger model
3. Why is fine-tuning described as an unreliable way to add factual knowledge?
   • It cannot change model weights
   • It only works on image models
   • It is hard to update and can increase confident errors about rarely seen material
   • It always deletes the model's existing knowledge
4. Which situation most clearly favors fine-tuning?
   • You need a narrow task done at high volume and want a smaller, cheaper model to match a larger one
   • Your documents change every day
   • You want answers to cite source passages
   • You have not yet built any evaluation examples
5. In a RAG system giving wrong answers, what should you check before blaming the language model?
   • The model's training date
   • Whether the correct passage appears in the retrieved results
   • The number of GPU cores
   • The length of the system prompt only
6. What does LoRA do in the context of fine-tuning?
   • Retrieves documents from a search index
   • Compresses prompts to save tokens
   • Trains small adapter matrices instead of updating all model weights
   • Removes the need for training data
7. Which statement about combining the three methods is accurate according to the guide?
   • They are mutually exclusive and cannot be combined
   • Production systems often use a fine-tuned model reading retrieved context, driven by a good prompt
   • RAG replaces the need for any prompt
   • Fine-tuning must always come before prompting
8. What should drive the choice between prompting, RAG and fine-tuning?
   • Whichever method is newest
   • A representative evaluation set measuring each option
   • The vendor's default setting
   • The size of the training budget alone