RAG vs. Fine-Tuning vs. Prompt Engineering
Learn when to use each technique for customizing LLMs. This section provides a decision framework based on knowledge dynamism, cost, and desired precision.
Learning Goals
- Compare RAG with Fine-Tuning and Prompt Engineering.
- Apply a decision framework to choose the right customization method.
- Identify the trade-offs between static and dynamic model updates.
Customization Options: The Three Paths
When an LLM doesn't know your data, you have three main ways to fix it. Choosing the wrong one can waste thousands of dollars and weeks of engineering time.
- Prompt Engineering: Including facts directly in the conversation window.
- Fine-Tuning: Training the model on a specialized dataset to change its internal "parametric" weights.
- RAG: Building a system that fetches facts and injects them into the prompt automatically.
A common mistake is thinking Fine-Tuning is the best way to teach a model "new facts." In reality, Fine-Tuning is best for teaching a model a "new style" or "behavior," while RAG is best for "new facts."
The Technical Decision Matrix
| Feature | Prompt Engineering | Fine-Tuning | RAG |
|---|---|---|---|
| Setup Cost | Lowest | High | Moderate |
| Latency | Low | Low | Moderate (Retrieval overhead) |
| New Knowledge | Minimal (Context window) | Excellent (but static) | Excellent (Dynamic) |
| Accuracy | Good | Moderate (Hallucinations persist) | Highest (Grounded) |
| Security | None | Hard (Data baked into weights) | Easy (Access control on DB) |
RAG vs Fine Tuning - Which one should you use?
The custom behavior selection framework
- 1Step 1
Does the model need to learn a new specialized format/tone, or does it need to access a constantly changing database of facts?
- 2Step 2
If your data changes more than once a month, Fine-Tuning will likely be too expensive and slow to keep updated.
- 3Step 3
If the amount of data needed is small enough to fit in a single message, use Prompt Engineering.
- 4Step 4
If you have thousands of documents and need high precision, RAG is the industry-standard choice.
Knowledge Check
Which method is best for teaching a model a specific "brand voice" or "creative style"?