Fine-Tuning vs Prompt Engineering
Prompt engineering gives quick gains, but domain-sensitive tasks often require deeper alignment. Fine-tuning helps the model learn terminology, tone, and constraints that prompts alone cannot reliably enforce.
Data Quality First
Fine-tuning quality is bounded by dataset quality. Label consistency, edge-case representation, and annotation standards matter more than dataset size alone.
Building a Reliable Training Set
Create datasets that represent real tasks:
- Customer support transcripts
- Internal policy and compliance samples
- Domain-specific Q&A pairs
Example Evaluation Schema
{
"factual_accuracy": 0.0,
"policy_compliance": 0.0,
"hallucination_rate": 0.0,
"latency_ms": 0
}Production Concerns
Fine-tuned models can drift over time as business language evolves. Build periodic evaluation cycles and fallback routing for uncertain outputs.
A model that was accurate six months ago may fail silently today if your domain changed.
Deployment Strategy
Use phased rollout: 1. Shadow mode on historical traffic 2. Partial traffic split with strict monitoring 3. Full rollout with rollback switch