Fine-Tuning, RAG or Prompting: Picking the Wrong AI Method Can Cost Thousands

Enterprises frequently default to fine-tuning AI models when simpler, cheaper alternatives would better solve their problem, according to a cost-focused analysis. The three main approaches — prompting, retrieval-augmented generation (RAG), and fine-tuning — each address a distinct category of issue rather than being interchangeable options. Prompting is best for shaping tone and format, costs little beyond token usage, and can be adjusted instantly in production. RAG is the appropriate choice when a model lacks access to specific or up-to-date information, typically costing £15,000–£60,000 to build. Fine-tuning, the most expensive option at £20,000–£80,000, is suited to teaching consistent behavioural patterns but is ineffective at injecting factual knowledge, which it cannot update, audit, or reliably cite.