Fine-Tuning

Fine-tuning takes a pre-trained foundation model and trains it further on a curated dataset of examples specific to your use case. This process adjusts the model's weights to improve performance on your target task while retaining the general capabilities it learned during pre-training. Fine-tuning is significantly cheaper and faster than training a model from scratch because you are building on top of existing knowledge.

There are several approaches to fine-tuning. Full fine-tuning updates all model parameters but requires significant compute resources. Parameter-efficient methods like LoRA (Low-Rank Adaptation) and QLoRA update only a small subset of parameters, making fine-tuning accessible on consumer hardware. Instruction tuning trains the model on example input-output pairs in a conversational format, while RLHF (Reinforcement Learning from Human Feedback) aligns model behavior with human preferences.

Fine-tuning is most valuable when you need consistent behavior patterns, specialized domain knowledge, or a particular output format that prompt engineering alone cannot reliably achieve. However, it requires high-quality training data and can sometimes degrade the model's general capabilities if not done carefully. Many teams find that RAG combined with good prompt engineering eliminates the need for fine-tuning in most practical applications.