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One of the most common questions we get from leadership teams is: "Can we train the model on our data?" It sounds sensible. If the model knew your policies, guidance, and history, wouldn't it just answer correctly?
In practice, most teams don't need fine‑tuning first. They need the model to access the right information, cite it, and stay up‑to‑date. That's what RAG is for.
RAG re‑indexing
fine‑tune cycle
RAG with citations + logs
Fine‑tuning changes how a model behaves (format, style, task patterns). RAG changes what the model can reference by retrieving relevant documents and adding them to the prompt at runtime.
RAG wins when you care about freshness, citations, and access control. That's why it's the default choice in many government and regulated environments.
Fine‑tuning is valuable when your primary need is consistent behaviour:
But it still needs evals. The risk is that a tune improves one set of behaviours while degrading another (silent regression).
| Option | When to Use | Trade-offs |
|---|---|---|
| Prompt-only | Low volume, low stakes, small scope, early experimentation | Hard to maintain; brittle; limited freshness and auditability |
| RAG | Need fresh/private knowledge, citations, and access control | Requires ingestion + retrieval pipeline; must be monitored and tuned |
| Fine‑tuning | Need consistent format/style; narrow tasks; stable requirements | Training cycles; versioning; can regress silently without eval gates |
| Hybrid (common) | RAG for knowledge + tuning for consistency + routing for cost | More moving parts; requires ownership and governance |
Long context helps for one-off analysis (paste a document, get an answer). It doesn't replace a knowledge system. For repeated usage, context becomes expensive, slow, and hard to control. RAG lets you keep prompts smaller, add access filters, and attach citations.
Start by writing 30–50 golden questions your system must answer correctly (with citations if needed).
If you can’t define success, you’ll debate opinions forever.
Prototype RAG first for knowledge-heavy use cases; implement access control early.
Teams often bolt on permissions later—this is painful and risky.
Add fine‑tuning only if prompting can’t reach the required consistency or format.
Fine‑tuning without eval gates is a regression generator.
Measure: retrieval quality, grounding, safety, latency, and cost per completed task.
Only measuring ‘token cost’ misses the real economics of throughput.
If your goal is "accurate answers grounded in our current data," start with RAG. If your goal is "consistent behaviour and format," consider fine‑tuning. In many real deployments, you'll use both, plus eval gates and cost controls.
MLX provides RAG building blocks (ingestion, retrieval, citations) and workflow components to move from prototypes to production safely.
Explore MLX →