RAG vs Fine-Tuning: Which Does Your Business Need?
We've built both. Here's when RAG makes sense, when fine-tuning wins, and when you need neither.
The Quick Version
Use RAG when your AI needs to reference specific, changing documents (company knowledge base, product docs, legal documents).
Use fine-tuning when you need the AI to behave in a specific way consistently (brand voice, domain-specific reasoning, structured outputs).
Use neither when a well-crafted prompt with examples does the job. Seriously - start here.
What is RAG?
Retrieval-Augmented Generation. Instead of hoping the AI "knows" your content, you feed it relevant documents at query time.
How it works:
We built Knoah using this exact approach. Teams upload their docs, and the AI answers questions with source citations.
Best for: Knowledge bases, customer support, internal docs search, legal document Q&A.
Cost: $8K–$15K to build. Minimal ongoing API costs.
What is Fine-Tuning?
Training a model on your specific data so it "learns" patterns, tone, and domain knowledge.
Best for: Consistent brand voice, domain-specific classification, structured data extraction.
Cost: $5K–$20K depending on dataset size and iteration cycles. Higher ongoing costs (custom model hosting).
Our Honest Take
90% of businesses that think they need fine-tuning actually need RAG - or just better prompts.
RAG is cheaper, faster to build, and easier to update (just add new documents). Fine-tuning is powerful but overkill for most use cases.
Not sure which you need? Let's talk - we'll recommend the simplest approach that actually works.