AI information
Upgrading LLMs: Fine-Tuning vs RAG
Compare fine-tuning and RAG by purpose, cost, speed, maintenance, and security, with guidance on when to use each.
CodeFree Team
support@codefreeai.studio
Key Comparison
To make a clear choice, separate what each approach is fundamentally for. The high‑level trade‑offs are:
| Category | Fine‑Tuning | RAG |
|---|---|---|
| Purpose | Improve style/tone; task‑specific | Connect up‑to‑date/internal knowledge; increase factuality |
| Data | Requires curated labeled datasets | Works with unstructured docs (PDFs, crawls) |
| Cost/Speed | High training cost; slower | Scales after initial infra build |
| Maintenance | Periodic retraining | Update data sources; reflect changes immediately |
| Security/Governance | Risk of data leakage | Easier access control within company networks |
When to Use What
- Brand voice or writing style → Fine‑tuning
- Answers grounded in latest policies/prices/docs → RAG
- Best of both worlds: “Light fine‑tuning + RAG” for quality and factuality
Cost and Operations
- Training costs: Fine‑tuning consumes GPU/engineering time; labeling is recurring.
- Serving costs: Larger models/longer contexts increase token spend; RAG trims context via retrieval.
- Change management: Policies/products change frequently—RAG updates via ingestion; fine‑tuning needs retraining cycles.
What to Choose (Quick Guide)
- Need brand voice or task style? → Fine‑tuning
- Need factual, up‑to‑date answers from internal docs? → RAG
- Need both? → Lightweight fine‑tuning for style + RAG for grounding
Implementation Blueprint
Safe, fast learning loop:
- Start with RAG to remove hallucinations and fill knowledge gaps.
- Add small‑scale fine‑tuning (SFT/LoRA) for tone or specific tasks.
- Measure with objective metrics (faithfulness, relevance, latency, cost) and iterate.
Risks and Mitigations
- Data leakage (Fine‑tuning): Minimize data; consider synthetic data; isolate training infra.
- Stale knowledge (Fine‑tuning): Schedule retraining; use RAG for volatile facts.
- Retrieval drift (RAG): Monitor retrieval quality; re‑evaluate embeddings; refresh indexes.
