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mediumAI Engineering

What is the difference between fine-tuning, LoRA and prompt engineering?

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01

Understand the problem

Three ways to change model behaviour — weights, adapters or context — and how to pick.

fine-tuningloraprompt-engineeringadaptation
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

Stuck? Beam a request up — the console returns a conceptual nudge that guides your logic without spoiling the implementation.

03

Study the solution

Prompt engineering changes behaviour via the input (instructions, examples, retrieved context) with no training. Full fine-tuning updates all weights on your dataset; LoRA trains small low-rank adapter matrices instead, giving most of the benefit at a fraction of the cost. Rule of thumb: start with prompting (+RAG for

Solution ready — 2 min read

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04

Read the code

LoRA in one glance (PEFT-style)
from peft import LoraConfig, get_peft_model

config = LoraConfig(r=16, lora_alpha=32,
                    target_modules=["q_proj", "v_proj"],
                    task_type="CAUSAL_LM")
model = get_peft_model(base_model, config)
model.print_trainable_parameters()
# trainable params: 4.2M || all params: 7B || trainable%: 0.06
05

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