The most common request we get at elitics.io is: "We want to fine-tune Llama 3 on our company PDFs so it knows our business."
This sounds logical, but it is almost always the wrong engineering decision. Fine-tuning is expensive, slow, and does not solve the problem of "knowledge." In 2026, the smart money is on RAG (Retrieval-Augmented Generation).
The Medical Student Analogy
Fine-Tuning is like sending a student to medical school. They memorize the textbooks. If protocols change next week, the student doesn't know until they go back to school (re-training).
RAG is like giving a smart student an open-book exam. They don't memorize the answers; they know how to look them up in the textbook (your database) instantly. If you update the textbook, their answers update immediately.
Why Fine-Tuning Fails for "Knowledge"
Fine-tuning changes the behavior of a model, not necessarily its facts. It is excellent for teaching a model to speak in a specific tone (e.g., "Answer like a pirate" or "Output valid JSON"), but it is terrible for factual recall.
The Hallucination Problem
A fine-tuned model doesn't cite sources. It just "dream" the answer based on probabilities. You cannot audit where the information came from.
The Freshness Problem
Your sales data changes every minute. You cannot fine-tune a model every minute. RAG queries your live database in real-time.
The Winning Stack: RAG + Vector DB
For 95% of enterprise use cases (Customer Support, Legal Review, Internal Search), the architecture should be:
When SHOULD you Fine-Tune?
We aren't saying never fine-tune. It has specific use cases:
- Domain Specific Languages
Teaching a model a proprietary coding language or obscure medical terminology schema.
- Brand Voice
Ensuring the model speaks exactly like your brand guidelines (e.g., "Helpful, witty, concise").
Verdict: Start with RAG. It's cheaper, faster, and more accurate. Only fine-tune if RAG fails to capture the "vibe."
Enjoyed this perspective? Share it with your team.