FM Fine-Tuning vs. RAG: Which to Choose When in the AIP Exam?
Explore how to decide between fine-tuning foundation models and using retrieval-augmented generation (RAG) based on exam question signals. Learn to evaluate business and operational constraints to choose the best architectural approach for improving GenAI model outputs on AWS, focusing on factors like data freshness, cost, accuracy, and governance.
One of the most common architectural decisions tested on the AIP exam is whether a problem should be solved with foundation model fine-tuning or with retrieval-augmented generation. Both approaches aim to improve the quality and reliability of model outputs, but they do so in fundamentally different ways. As a result, choosing the wrong one often leads to answers that appear reasonable but do not align with the constraints described in the question.
The exam presents business scenarios, operational requirements, or system limitations that imply one approach over the other. Understanding how to interpret these signals is an important exam skill and the primary focus of this lesson. This lesson teaches how to decode exam question signals that implicitly favor fine-tuning or RAG, even when both options appear technically valid.
Why the AIP exam tests fine-tuning vs. RAG decisions
The AIP exam emphasizes architectural ...