📝 Blog • Geniuspace® algorithm
LoRA Fine-Tuning for LLMs: When, Why and How (Enterprise View)
A practical guide to LoRA fine-tuning: decision criteria vs RAG, data governance, evaluation, deployment patterns and safety controls.
🛡️ Governance
📜 Evidence trail
☁️ On‑prem/VPC/Edge
Key takeaways
- Use LoRA when behavior must be stable and repeatable.
- Prefer RAG for fast updates and traceable knowledge grounding.
- Data governance: access, retention, provenance and approvals.
- Evaluation: robustness, bias, safety and regression tests.
LoRA vs RAG (procurement-friendly decision)
- LoRA: change behavior; good for consistent style/policies.
- RAG: change knowledge; good for auditability and freshness.
Enterprise guardrails
- Use curated datasets with approvals.
- Run regression tests before promotion.
- Track model adapters and versions.
What to document
Training data lineage, evaluation reports, release notes, and rollback plans are typically required by large organizations.
Procurement note
If you want this to survive audits, insist on artifacts: requirements, evaluation gates, logs, incident procedures and reversibility clauses.