🚀 Generative AI Fundamentals – Part 6

⚙️ Fine-Tuning, LoRA, PEFT, RLHF & Model Alignment

Fine-tuning and model adaptation are important topics for GenAI Engineer, LLM Engineer, and Applied AI interviews.

1. What is Fine-Tuning?

Fine-tuning is the process of taking a pretrained model and training it further on a smaller, specialized dataset.

Example:

General LLM → Financial Documents → Fine-Tuning → Financial AI Assistant

The goal is to make the model perform better on a specific task or domain.

2. Pretraining vs Fine-Tuning

Pretraining: Initial model training, Very large dataset, Learns general patterns, Expensive, Creates foundation model

Fine-Tuning: Additional training, Smaller specialized dataset, Learns specific behavior, Relatively cheaper, Adapts foundation model

Simple Example:

Pretraining: Learn general English.

Fine-tuning: Learn how to answer banking customer-support questions.

3. When Should You Fine-Tune an LLM?

Fine-tuning can be useful when you need:

✅ Consistent output format

✅ Specific writing style

✅ Domain-specific behavior

✅ Specialized classification

✅ Task-specific performance

✅ Consistent instruction following

Example: A company wants every support response to follow a specific format. Fine-tuning may be more appropriate than repeatedly putting the same style instructions into prompts.

4. When Should You NOT Fine-Tune?

Fine-tuning isn't always the best solution.

Avoid fine-tuning when the main problem is changing knowledge.

Example: A company has thousands of frequently changing policies.

Instead of continuously fine-tuning the model, use: RAG → Retrieve the latest policy → Generate answer

Rule: RAG changes the information available to the model; fine-tuning changes how the model behaves.

5. What is Parameter-Efficient Fine-Tuning (PEFT)?

PEFT allows you to adapt a large model without updating all of its parameters.

Large Frozen Model + Small Trainable Parameters → Adapted Model

Benefits: Lower GPU requirements, Lower training cost, Faster training, Smaller adaptation files

6. What is LoRA?

LoRA stands for Low-Rank Adaptation.

It is a popular PEFT technique that freezes the original model weights and adds small trainable matrices.

Original Model → Frozen + LoRA Adapters → Fine-Tuned Model

7. What are LoRA Adapters?

LoRA adapters contain the learned changes needed for a particular task.

Base Model → Finance Adapter, Medical Adapter, Coding Adapter

The same base model can therefore be adapted for different applications.

8. What is QLoRA?

QLoRA combines: Quantization + LoRA

The base model is loaded using lower-precision representations while LoRA adapters are trained.

Benefits: Lower memory requirements, Lower hardware cost, Makes large-model fine-tuning possible on more limited hardware

9. What is Transfer Learning?

Transfer learning means taking knowledge learned from one task and applying it to another related task.

General Language Model → Transfer Learning → Legal Document Model

10. What is RLHF?

RLHF stands for Reinforcement Learning from Human Feedback.

It uses human preferences to improve model behavior.