Moderate Fine-tuning vs RAG Question 105 of 223

What is LoRA or PEFT?

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

PICTURE THIS: DATA SPLIT

Train 70%Val 15%Test 15%

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

Simple meaning

Parameter-efficient fine-tuning trains small adapter matrices instead of all weights.

WHY — Tokens instead of words?

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.

STEPS — What happens step by step?

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

1
Parameter-efficient fine-tuning trains small
adapter matrices instead of all weights.
2
LoRA is the common
method: low-rank updates on attention or MLP projections.
3
You get domain fit
at a fraction of full fine-tune cost and can swap adapters per tenant.
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.

EXAMPLE — See it in action

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

Say this line

“LoRA is the common method: low-rank updates on attention or MLP projections.”

Break into beats

LoRAisthecommonmethodlow

Speaking order

2987408337471632900

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

Key takeaway

Parameter-efficient fine-tuning trains small adapter matrices instead of all weights. LoRA is the common method: low-rank updates on attention or MLP projections.