Technical interview guide

Fine-Tuning & Adaptation

Adapting a pretrained LLM's behavior via further training, and how that differs from prompting or RAG.

Read: 45 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: AI EngineerAgentic AI EngineerForward Deployed Engineer (FDE)

Scope: Transformer, InstructGPT, LoRA, QLoRA, DPO, LIMA, Model Cards, Datasheets, Hugging Face PEFT/Transformers/TRL, NIST AI RMF, and OWASP supply-chain references reviewed 2026-09-06.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-03

QA-1

Choose fine-tuning versus prompt, RAG, tools, or code.

QA-2

How do Full Fine-Tuning and Parameter-Efficient Fine-Tuning differ across memory footprint, catastrophic forgetting, and multi-task serving?

QA-3

How do you curate, filter, and deduplicate synthetic instruction-tuning datasets generated via methods like Self-Instruct or Evol-Instruct?

QA-4

Create leakage-resistant dataset splits.

QA-5

Prepare instruction data for supervised fine-tuning.

QA-6

Design an SFT training run and stopping rule.

QA-7

Choose and configure LoRA adapters.

QA-8

Validate a QLoRA pipeline.

QA-9

Tune PEFT capacity systematically.

QA-10

Detect and mitigate forgetting during fine-tuning.

QA-11

Build a preference dataset and DPO experiment.

QA-12

Audit preference-label quality.

QA-13

Define a fine-tuning evaluation scorecard.

QA-14

Design experiment tracking and artifact lineage.

QA-15

Secure the fine-tuning model supply chain.

QA-16

Assess and reduce fine-tuning privacy risk.

QA-17

Defend fine-tuning against poisoning and backdoors.

QA-18

Promote a LoRA adapter into serving.

QA-19

Write a fine-tuned model card for release.

QA-20

Roll out a fine-tuned model safely.

QA-21

Turn production feedback into governed training data.

QA-22

Respond to a fine-tuning safety regression.

QA-23

Operate a distributed fine-tuning job reliably.

QA-24

Design multi-adapter serving safely.

QA-25