AI & Technical question

What are the latest techniques available to customize/fine-tune LLMs? Explain one in detail to your leader, who knows very little about ML/AI.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

AI and technical depth plus communication: can you explain a real fine tuning technique clearly to a non technical leader without losing accuracy.

Name the current landscape briefly: full fine tuning, parameter efficient methods like LoRA, and prompt based approaches like retrieval augmented generation.
Pick one to explain in depth, LoRA (low rank adaptation), since it is widely used and has a genuinely simple core idea.
Explain LoRA in plain terms: instead of retraining the entire model's billions of parameters, you train a small set of added parameters that adjust the model's behavior, which is far cheaper and faster.
Use an analogy for the non technical leader, like adjusting a few dials on a finished machine instead of rebuilding the whole machine.
State the trade off honestly: LoRA is cheaper and faster but generally less powerful than full fine tuning for very large behavior changes.
Connect it to a business decision, when to use LoRA (frequent, lower budget updates) versus full fine tuning (rare, high stakes changes).

Picks one technique to explain deeply rather than superficially naming several, matching what the question actually asks.
Uses a clear, non technical analogy that would genuinely land with a leader unfamiliar with ML.
States the real trade off, cost versus power, honestly instead of overselling the technique.

Naming many techniques with no depth on any single one, missing the explain one in detail instruction.
Using jargon like rank decomposition matrices without ever translating it for a non technical audience.

When would you recommend full fine tuning instead of LoRA?
How would you explain the cost difference between these approaches to a budget owner?