Your question is Fine-Tuning vs Retrieval Trade-offs. 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).
What are the primary trade-offs when choosing between fine-tuning a model versus implementing a retrieval-augmented approach?
Explain how you would make the decision for an LLM application with changing knowledge, quality requirements, and production cost and latency constraints. Address evaluation, hallucination risk, data privacy, maintenance, and prompt injection. Provide a practical recommendation framework rather than treating either technique as universally superior.