Introduce LLM Fine-Tuning from absolute foundations

About the speaker - https://www.linkedin.com/in/anupamai-cto/

## A. Start with the Foundations

• What is an LLM?
• How does an LLM “think”?
• Basics of:
• tokens
• context window
• reasoning
• chain-of-thought (high level)
• strengths vs weaknesses of different LLM families
• Why fine-tuning evolved after RAG and prompting
• The relationship between:
• Prompting
• RAG
• Fine-tuning
• Hybrid approaches

## B. Why Fine-Tuning? (Problem-First Approach)

Cover:

• Where prompting fails
• Where RAG fails
• What kinds of problems require fine-tuning:
• domain-specific style
• compliance
• predictable structured outputs
• specialized tasks (healthcare, fintech, etc.)
• Why enterprises create domain LLMs

## C. What is Fine-Tuning?

Explain in simple terms:

• Fine-tuning = optimizing model parameters for a specific domain/task
• A form of teaching the model deeper behaviours
• Relationship to:
• base models
• instruction tuning
• SFT
• DPO/RLHF (very high level)

## D. Types of Fine-Tuning / Techniques

Beginner-friendly introduction to methods:

• Full fine-tuning (concept only)
• Parameter-efficient fine-tuning (PEFT)
• LoRA
• QLoRA
• Prefix/Prompt tuning
• What “adapters” are
• What changes internally in an LLM

## E. Key Concepts in Simple Language

• Inference vs training
• Training on GPU/CPU: what changes and why
• Datasets required
• Tokenization
• How examples shape the model’s behaviour
• Importance of clean, high-quality data
• Overfitting & hallucination behaviour

## F. Practical Flow: Steps of Fine-Tuning

Equivalent to the “5 steps of RAG” flow:

1. Define the task
2. Collect domain dataset
3. Clean/prepare (format, sample, structure)
4. Choose the fine-tuning method (LoRA, QLoRA)
5. Train
6. Validate performance
7. Deploy & evaluate