High Fine-tuning vs RAG Question 171 of 223

What matters more for SFT quality: data volume or data quality?

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

PICTURE THIS: AN LLM TURN

Text inTokens

TransformerAttention

Text outNext token

Simple meaning

A few thousand clean, diverse, on-policy examples usually beat a noisy dump of tickets.

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
A few thousand clean,
diverse, on-policy examples usually beat a noisy dump of tickets.
2
Dedup, fix label errors,
and cover refusal and tool-use cases.
3
Garbage SFT teaches the
model your bugs with high confidence.
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

“Dedup, fix label errors, and cover refusal and tool-use cases.”

Break into beats

Dedupfixlabelerrorsandcover

Speaking order

2987408337471632900

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

Key takeaway

A few thousand clean, diverse, on-policy examples usually beat a noisy dump of tickets. Dedup, fix label errors, and cover refusal and tool-use cases.