Fine-Tuning vs RAG: Which to Actually Build
Fine-tuning teaches a model how to behave. RAG teaches it what to know right now. They solve different problems. Building the wrong one wastes months. Here's the decision framework.
Fine-Tuning
Modifies the model's weights to change how it behaves. The model learns your domain's terminology, output format, tone, and task patterns from examples.
RAG (Retrieval-Augmented Generation)
Injects relevant documents into the model's context at query time. The model's weights are unchanged; it's given information to reason about at each request.
Feature-by-Feature Comparison
| Capability | Fine-Tuning | RAG |
|---|---|---|
| Teaches domain terminology | ||
| Enforces consistent output format | ||
| Improves brand voice / tone | ||
| Access to current facts (live data) | ||
| Handles frequently-changing knowledge | ||
| Reduces prompt length (and cost) | ||
| Works with private documents | ||
| Improves task-specific accuracy | Partial | |
| No code required (with Ertas) | Varies |
Which Technique for Which Use Case
Customer support chatbot
Fine-tune for tone and escalation behavior. RAG for current product specs and policies that update frequently.
Internal document Q&A
The entire value is answering questions from documents. RAG retrieves the right doc at query time. Light fine-tuning optional for consistent formatting.
Brand-voice content generation
Voice and style are behavioral patterns learned from examples. Fine-tune on existing brand content. Add RAG if content needs product details.
Code review assistant
Team coding conventions are stable patterns — fine-tune on reviewed code examples. RAG adds little here beyond a well-prompted base model.
Sales prospect research
Constantly-changing information. Connect RAG to live data sources. Fine-tuning doesn't help when the problem is data access.
Compliance document classification
Classification into fixed categories is a Tier-1 task. Fine-tuned 7B models achieve 90-95% accuracy — often beating GPT-4 prompts on narrow domains.
Common Questions
When should I use fine-tuning?
When the failure mode is wrong behavior — wrong tone, wrong style, wrong output format, wrong domain terminology. Fine-tuning changes how the model acts.
When should I use RAG?
When the failure mode is wrong facts — the model doesn't know current product info, policy details, or private records. RAG gives the model access to information at query time.
Can I use both together?
Yes — and for most production deployments, you should. Fine-tune for behavior and style; use RAG for current facts. They solve different problems and work well together.
How much data do I need to fine-tune?
As little as 100-500 high-quality examples for simple tasks. 500-2,000 examples for complex domain tasks. More data consistently improves quality.
Start fine-tuning your first model
Ertas makes fine-tuning accessible without ML expertise. Upload data, pick a base model, export GGUF. Early-bird at AU$14.50/month.
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