Fine-tuning vs RAG

Fine-tuning vs RAG

When you want an AI model to work with specific knowledge or behave in a specific way, two approaches come up repeatedly: fine-tuning and RAG. They solve different problems, and often work best together. Understanding when to use each is a key practical skill.

What Each Approach Does

Fine-tuning adjusts the model's weights on a new dataset. The model "internalises" the information. After fine-tuning:

• The knowledge is part of the model
• No retrieval step needed at runtime
• The model's behaviour and style can be reshaped
• But the training data is frozen — updates require re-training

RAG keeps the model's weights fixed and injects relevant information at query time. The model reads from the provided context:

• Knowledge can be updated instantly (just update the document store)
• Sources are visible and citable
• Works with private or constantly-changing data
• Requires retrieval infrastructure and good chunking

When to Use Each

Use RAG when:

• You have a large or frequently-updated knowledge base
• You need to cite sources or show where answers came from
• The information is proprietary or sensitive
• You want to add knowledge without retraining
• The domain is factual and retrieval quality will be high

Use fine-tuning when:

• You need to change the model's tone, style, or format
• You want to teach a task pattern, not just facts
• You need the model to follow domain-specific conventions consistently
• You have high-quality labelled input/output pairs
• Latency matters and retrieval overhead is a problem

Use both when:

• You want a model with domain-specific style (fine-tuned) that also has access to current documents (RAG)
• Many production AI systems do exactly this

The Practical Reality

For most teams building AI features, RAG is the right starting point:

• No GPU training required — just embed and store documents
• Faster to iterate — update docs, not model weights
• More transparent — you can see what was retrieved
• Cheaper — fine-tuning costs vary but can be significant for large models

Fine-tuning is worth considering when:

• You've already tried RAG and it's not meeting quality targets
• You have a very specific formatting or behaviour requirement
• You can generate large quantities of high-quality training examples
• You have the infrastructure to manage fine-tuned model versions

The rule of thumb: RAG first, fine-tune later if needed.