RAG vs fine-tuning: when to use which for your product

One of the most common questions we get: should we fine-tune or use RAG?

The answer depends on your use case, budget, and timeline. Let's break it down.

Retrieval-Augmented Generation (RAG) wins when: • You need answers from proprietary/fresh data • Your data changes frequently • You're on a tight timeline (RAG is weeks, fine-tuning is months) • You have limited budget • You care about explainability (RAG shows sources)

Fine-tuning wins when: • You need specialized reasoning (medical diagnosis, code generation) • You have enough domain-specific training data (1000+ examples minimum) • You need consistent style/tone across all outputs • Cost-per-inference is important (fine-tuned models are cheaper to run)

Real-world comparison:

RAG system (8 weeks, $15k): Used for a consulting firm's internal knowledge base. Delivers accurate answers 94% of the time. Cost to maintain: $200/month.

Fine-tuned model (16 weeks, $45k): Used for a legal tech company to generate contracts. Achieves 97% accuracy on legal language. Cost to maintain: $50/month.

Most companies should start with RAG. It's faster, cheaper, and more maintainable. Fine-tuning is for specialized use cases where the ROI is clear.