Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
Which TWO are advantages of using Retrieval-Augmented Generation (RAG) over fine-tuning?
⚠ Common exam trap
Google often tests the misconception that RAG is always faster or simpler than fine-tuning, but candidates must remember that retrieval adds latency and requires careful data preprocessing, making options B and C tempting but incorrect.
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
✓
No need to retrain the base model
RAG retrieves relevant external knowledge at inference time without modifying the base model's parameters, eliminating the need for retraining (option A). This contrasts with fine-tuning, which requires updating model weights through additional training cycles. Because the knowledge lives in an external, updatable index rather than in the model weights, RAG is also better suited for rapidly changing knowledge bases (option E): documents can be added, updated, or removed without any retraining, whereas fine-tuning would require a new training run each time the knowledge changes. RAG thus preserves the original model while augmenting its output with up-to-date information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
No need to retrain the base model
Why this is correct
Correct: RAG works with the pre-trained model and a retrieval system.
- ✗
Requires less data preparation
Why it's wrong here
RAG requires indexing documents, which also needs preparation.
- ✗
Lower inference latency
Why it's wrong here
RAG adds retrieval latency, so it's often slower.
- ✗
More secure because model weights are not modified
Why it's wrong here
Both RAG and fine-tuning can be secure; weight modification alone doesn't determine security.
- ✓
Better suited for rapidly changing knowledge bases
Why this is correct
Correct: RAG can retrieve the latest documents without retraining.
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