RAG wins when your data changes weekly or faster, citations are mandatory, and your ML team is early-stage. It keeps the base model frozen, embeds your corpus into a vector store, and fetches only the relevant chunks at query time — giving you verifiable outputs and straightforward data governance without a training run.

Fine-tuning shines when your domain has highly specialised vocabulary, a strict output format, or latency requirements below 300 ms. LoRA and QLoRA adapt only a small fraction of model weights, keeping training costs manageable ($1 K–$25 K per run). The resulting model is faster at inference and requires no retrieval hop, but it cannot incorporate new information without a retraining cycle.

Long-context prompting stuffs your entire relevant document set into the model's context window — up to 1 M tokens with models like Gemini 1.5 Pro or Claude 3.5. It requires zero training, zero vector infrastructure, and delivers an answer in a single API call. It is the right default for small corpora (< 500 documents) with low query volumes, where simplicity outweighs per-query token cost.

Hybrid (also called RAFT) combines RAG's real-time retrieval with fine-tuning's domain adaptation. The model is trained to reason over retrieved documents — significantly reducing hallucination compared to RAG alone while preserving the ability to incorporate new information. It is the highest-performance option but also the most expensive and operationally complex. Recommended only when capability ≥ 3 and budget ≥ $15 K/mo.