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RAG vs fine-tuning: choosing an approach for a prototype

A decision framework for comparing retrieval-augmented generation and fine-tuning against data freshness, traceability, and behavior requirements.

Mar 14, 2026 8 min read

Why this matters

RAG and fine-tuning solve different problems. RAG helps with changing knowledge and traceability, while fine-tuning helps with stable behavior and specialized style.

Recommended approach

Start with a decision tree: if facts change weekly or need citations, use RAG first. If behavior is stable and you need deterministic formatting, evaluate fine-tuning after RAG baseline performance.

Implementation checklist

  • Estimate data freshness requirements
  • Test citation reliability with RAG
  • Validate output structure using schema constraints
  • Only fine-tune if baseline fails target KPIs

Metrics to track

  • Hallucination rate
  • Citation hit rate
  • Cost per successful task
  • Latency at p95

Key takeaway

RAG is the default starting point for most business apps; fine-tuning is an optimization step, not a first step.

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