High Fine-tuning vs RAG Question 173 of 223

How would you decide between continual fine-tuning and retrieval for a new knowledge domain?

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

If facts churn, require citations, or are tenant-specific, retrieve.

WHY — Tokens instead of words?

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

This is a process

question about Fine-tuning vs RAG.

Panels listen for order,

trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.

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
If facts churn, require
citations, or are tenant-specific, retrieve.
2
If the domain needs
new skills, jargon fluency, or a stable procedure, fine-tune or use adapters.
3
Run a bake-off on
the same eval set with cost and stale-document tests included.
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

“If the domain needs new skills, jargon fluency, or a stable procedure, fine-tune”

Break into beats

Ifthedomainneedsnewskills

Speaking order

2987408337471632900

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

Key takeaway

If facts churn, require citations, or are tenant-specific, retrieve. If the domain needs new skills, jargon fluency, or a stable procedure, fine-tune or use adapters.