Your question is Fine-Tuning vs RAG. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain the architectural differences between fine-tuning a pre-trained model and using RAG (Retrieval-Augmented Generation) in a production environment.
Explain how data flows through each architecture, when knowledge is stored in model weights versus an external retrieval system, and how you would operate and evaluate each approach. Address update frequency, latency, cost, hallucination risk, access control, and prompt injection. Include a practical recommendation for choosing between them, or combining them, for a production LLM feature.
