Regulated and confidential workflows — assessments, case notes, internal knowledge — often cannot use public LLM APIs. The alternative is not a mysterious research lab; it is a boring pipeline.
Prepare domain data carefully. Fine-tune with parameter-efficient methods when full training is wasteful. Export to a runtime your ops team can actually run. Put a thin API in front so products never import training notebooks.
Privacy does not replace evaluation. A private wrong answer is still wrong — and harder to notice if nobody built golden tests.
A pipeline you can hand to ops
Separate training environments (GPU, experiment tracking, checkpoints) from serving environments (pinned model artifacts, health checks, horizontal scale).
GGUF/Ollama-style packaging is often enough for private product inference when latency budgets are reasonable and hardware is owned.
Keep products away from training drama
Applications should call an inference contract: input schema, safety filters, timeouts, and version IDs.
When the model improves, bump a version and re-run evaluation — do not silently swap weights behind a stable URL.
Threat model the obvious things
Control who can prompt the model with sensitive corpora. Log access. Prevent training sets from leaking into unrelated apps sharing the same cluster.
Treat model artifacts like binaries with provenance — not like loose files on a shared drive.
Takeaways
- Private LLM success is mostly packaging and governance.
- Fine-tune → export → API → evaluate is the loop.
- Version models the way you version services.