Easy Fine-tuning vs RAG Question 44 of 223

What is fine-tuning an LLM?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

Train 70%Val 15%Test 15%

Fit on train, tune on val, report on test once.

Fine-tuning continues training on your examples so weights change.

LLMs use tokens (not full words) because it helps them:

Fine-tuning vs RAG questions

separate people who only read docs from people who shipped.

Keep it short, concrete,

and tied to GenAI / LLM work.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

Before you speak the answer, walk the interviewer through these steps:

1
Fine-tuning continues training on
your examples so weights change.
2
It can teach format,
tone, and domain phrasing.
3
It does not automatically
stay current unless you keep retraining on new facts.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

Here's a short line you can speak, broken into clear beats:

Say this line

“It can teach format, tone, and domain phrasing.”

Break into beats

Itcanteachformattoneand

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Fine-tuning continues training on your examples so weights change. It can teach format, tone, and domain phrasing.