What is Fine-Tuning
Fine-Tuning Explained
Fine-tuning takes a pre-trained LLM and retrains it on your own domain-specific data. Like a new engineer joining a company — they already know programming fundamentals, but the company spends months training them on internal systems and processes.
Types of fine-tuning
·Supervised Fine-Tuning — Uses labelled prompt-and-response pairs to shape model behaviour
·Continued Pre-Training — Unsupervised training on raw domain text to expand knowledge
·RLHF — Reinforcement Learning from Human Feedback — used by GPT-4 and Claude
·LoRA / QLoRA — Parameter-efficient fine-tuning — cheaper but still requires training runs
Fine-Tuning Problems
Why Fine-Tuning Is Not Always the Answer
✕Computationally expensive — Training large models requires serious GPU budget
✕Needs ML expertise — You need AI engineers and data scientists to execute this
✕Must repeat when data changes — Every data update requires another training run
✕Labelled data is expensive — You need 10,000 to 1,000,000 rows of curated examples
✕Hard to remove knowledge — Unlearning what the model knows is a research problem
Direct Comparison
Fine-Tuning vs RAG — Head to Head
Fine-Tuning
High GPU and compute cost Requires labelled dataset
Slow to update when data changes Needs ML team to execute
Risk of catastrophic forgetting
RAG
No training cost at all Uses raw documents as-is
Instant updates — add to vector store Developer friendly
Nothing baked into weights
Decision Guide
When to Use Each
Choose fine-tuning when
✓You need to permanently change the model's tone or style
✓You want to bake in deep domain expertise that never changes
✓Your data is completely static and will never need updating
Choose RAG when
✓Your data changes frequently — news, documents, product updates
✓You need Q&A over private documents without data exposure
✓Budget is limited and you need fast deployment
✓You want to reduce hallucination with source grounding
✦
Fine-tuning is powerful but expensive, slow, and rigid. RAG is cheaper, instant to update, and easier to build. For the vast majority of real-world applications — private data access, dynamic information, frequent updates — RAG is the smarter choice. Use fine-tuning only when you need to change the model's core behaviour permanently.