When fine-tuning makes sense
Fine-tuning is most valuable when you need a model to consistently match a specific style, handle domain-specific terminology, or produce structured output in a particular format, and prompt engineering alone isn't getting you there.
Examples: a model that writes in your exact brand voice across thousands of interactions, a classifier that categorises support tickets into your company-specific taxonomy, or a model that extracts fields from your industry's non-standard document formats.
Fine-tuning vs RAG
RAG is better when the goal is giving the model access to specific information (your policies, products, documentation). Fine-tuning is better when the goal is changing how the model behaves (its writing style, classification accuracy, output structure).
In practice, many production systems combine both: a fine-tuned model for consistent behaviour plus RAG for accurate, up-to-date information.
Cost and complexity
Fine-tuning requires curated training data (typically hundreds to thousands of high-quality examples), costs more than standard API usage, and needs periodic retraining as your requirements evolve. For most SMB use cases, well-crafted prompts and RAG deliver better ROI than fine-tuning.