Moderate Fine-tuning vs RAG Question 104 of 223

How do fine-tuning and RAG differ on knowledge freshness?

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-tuned facts are frozen until the next training run.

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.

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

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

Say this line

“RAG can pick up a document minutes after ingest.”

Break into beats

RAGcanpickupadocument

Speaking order

2987408337471632900

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

Fine-tuned facts are frozen until the next training run. RAG can pick up a document minutes after ingest.