RAG vs Fine-Tuning
RAG (retrieval-augmented generation) grounds a model in your data at query time and is the right first choice for most enterprise AI. Fine-tuning bakes behaviour into the model itself and wins for consistent style, format or specialised skills. In practice, strong systems often use both.
| Criterion | RAG | Fine-Tuning |
|---|---|---|
| Uses your latest data | Yes — retrieved live | No — frozen at training time |
| Reduces hallucination | Strong — answers cite sources | Weak on facts |
| Consistent tone / format | Prompt-dependent | Strong — learned behaviour |
| Upfront effort & cost | Lower — no training run | Higher — data prep + training |
| Update speed | Instant — change the index | Slow — retrain to change |
| Specialised skill/style | Limited | Strong |
| Data governance | Data stays in your store | Data absorbed into weights |
Choose RAG when
Answering from private, changing knowledge; source citations; fast iteration; keeping data in your own boundary — the default for enterprise assistants and search.
Choose Fine-Tuning when
Enforcing a consistent voice or strict output format, teaching a narrow skill, or reducing prompt size at very high volume.
The verdict
Start with RAG — it is cheaper, safer and updates instantly. Add fine-tuning only where you need behaviour prompting can't reliably deliver. Lazlo builds grounded RAG systems first, then fine-tunes where it measurably earns its keep.