RAG, Fine-Tuning, or Long Context: How to Pick the Right AI Architecture

The long-standing debate between retrieval-augmented generation (RAG) and fine-tuning has evolved into a three-way decision, with long-context prompting now a viable third option. Two key shifts drove this change: frontier models routinely support context windows of one to two million tokens, and LoRA-based fine-tuning has become fast and affordable enough for a single engineer to execute in a day. RAG remains the best fit when knowledge changes frequently and source citations are required, while fine-tuning addresses behavioral issues like tone and output formatting rather than factual gaps. Long-context prompting works well when the entire knowledge base is small enough to fit in a single prompt and queries are exploratory rather than high-volume. In practice, most production systems now use a hybrid approach, combining RAG for current, citable facts with a lightly fine-tuned model for style and format consistency.