Should I use RAG or fine-tuning for my AI project?

In most enterprise cases, RAG is the right starting point — it's faster to implement, easier to update, and doesn't require training infrastructure. Fine-tuning is better when you need consistent style or format, domain-specific reasoning, or latency optimization.

What is RAG and how does it compare to fine-tuning?

RAG (Retrieval-Augmented Generation) retrieves relevant documents at query time and feeds them to the LLM. Fine-tuning trains the model on your data. RAG keeps data fresh and auditable; fine-tuning bakes knowledge into the model for faster inference.

When should I fine-tune an LLM instead of using RAG?

Fine-tune when you need consistent output formatting, domain-specific reasoning patterns, latency below what RAG can achieve, or when your use case requires the model to internalize specialized knowledge rather than retrieve it.

AI Strategy May 5, 2026 8 min read

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