What is Model Fine-Tuning?

Fine-tuning modifies the internal weights of a foundation model using your proprietary data, creating a specialized model that inherently understands your business without requiring extensive prompting. The process involves four stages. Data Curation: assembling 500-5,000 high-quality input/output examples that represent ideal interactions (customer service responses, technical documentation answers, sales qualification scripts). Each example must be manually reviewed for accuracy, tone, and format. Data Formatting: converting examples into the model provider's training format (JSONL with system/user/assistant message pairs for OpenAI, or instruction/response pairs for open-source models). Training: uploading the dataset and running the fine-tuning job, which typically takes 1-4 hours and costs $25-$500 depending on dataset size and model. Evaluation: testing the fine-tuned model against a held-out evaluation set to measure improvement over the base model. The key decision is fine-tuning vs. RAG. Fine-tuning embeds knowledge into the model's weights, ideal for consistent formatting, brand voice, and behavioral patterns. RAG injects knowledge at inference time, ideal for factual accuracy on dynamic data. Most enterprise deployments use both: a fine-tuned model for tone and format consistency, augmented with RAG for up-to-date factual grounding.