Everyone claims their approach scales, but picking the wrong one wastes months and millions. As an AI architect building practical applications, what’s the decision matrix for compute/budget/use case: when fine-tuning beats RAG (domain-specific reasoning), when RAG crushes fine-tuning (dynamic knowledge), and when clever prompting extracts 90% value for free? Include real benchmarks, failure modes, and migration paths.
Vikram KumarBegginer
When do you actually fine-tune vs RAG vs prompt engineering and which wins most often?
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RAG wins 70% of cases: dynamic docs, low compute, easy updates (LlamaIndex benchmarks: 25% accuracy lift over base LLM). Fine-tune only for: style injection, strict safety, or compute-cheap inference (e.g., <1B params). Prompting maxes at instruction following + few-shot. Matrix: Does knowledge change frequently? RAG. Domain-specific reasoning >85% accuracy needed? Fine-tune. Migration: start prompting → RAG → LoRA fine-tune → full fine-tune. Monitor eval suites weekly.
Decision tree: Can RAG hit 90% accuracy on held-out eval? → RAG + hybrid search. Still <90%? → LoRA/PEFT fine-tune (1% compute of the full). Need causal reasoning/style? → Full fine-tune. Prompting solo only for <100 examples, simple classification (OpenAI evals: CoT prompting closes 60% gap to tuned). Failure modes: RAG hallucination (bad chunks), fine-tune catastrophic forgetting. Always: evals first, A/B in prod, rollback ready. RAG is most pragmatic 80% of the time.