Fine-Tuning

Why It Matters

Fine-tuning transforms a general-purpose LLM into a specialized tool. While prompting can get you surprisingly far, some use cases demand consistent behavior that prompts alone can’t guarantee, like specific output formats, domain terminology, or company-specific tone of voice.

The real question isn’t whether fine-tuning works (it does), but whether you need it. Fine-tuning is expensive in compute, requires clean training data, and can cause “catastrophic forgetting” where the model loses general capabilities. Most production applications should exhaust RAG and prompt engineering before reaching for fine-tuning.

For AI engineers, understanding fine-tuning means knowing when NOT to use it. The 2026 landscape offers efficient alternatives like LoRA and QLoRA that dramatically reduce costs, but the decision framework remains: can you solve this with better prompts or retrieval first?

Implementation Basics

Fine-tuning requires three core components:

1. Training Data You need examples of the input-output pairs you want the model to learn. Format matters. OpenAI uses JSONL with messages arrays, others use different schemas. Quality beats quantity; 100 excellent examples often outperform 10,000 mediocre ones.

2. Base Model Selection Start with a model close to your target capability. Fine-tuning a coding model for code tasks will always beat fine-tuning a general model. Consider size vs. performance tradeoffs, since fine-tuning a 7B model is much cheaper than fine-tuning a 70B model.

3. Hyperparameter Configuration Learning rate, epochs, and batch size significantly impact results. Too aggressive and you’ll overfit; too conservative and you’ll waste compute. Start with provider defaults and adjust based on validation loss.

The modern approach uses parameter-efficient methods like LoRA that only train a small subset of weights. This reduces costs by 10-100x while achieving comparable results. Full fine-tuning is increasingly rare outside of frontier model training.