· Nolwen Brosson · Blog · 4 min read
RAG vs Fine-Tuning vs SLM: How to Choose the Right AI Approach
When you want to integrate generative AI into a product, three options come up often: RAG, fine-tuning, and SLMs (Small Language Models). In practice, these are three different levers: one brings the right context into the prompt, another shapes the model’s behavior, and the third changes the economics and deployment.
Understanding the 3 approaches
RAG (Retrieval-Augmented Generation)
RAG consists of retrieving information from your data sources and providing it to the model at the moment it answers. It’s especially useful when the question involves information the base model doesn’t already know.
Example: A chatbot that answers employee questions based on the company’s entire knowledge base.
Fine-tuning
Fine-tuning adjusts a model so it follows a specific format, tone, instruction, or task type better, using a dataset of examples. It’s useful for making responses more consistent and more reliable on repeated patterns (classification, extraction, style, procedures).
Example: Further training a model specialized in health-related questions.
SLM (Small Language Models)
SLMs are smaller models than “giant” LLMs. They target cheaper deployments,sometimes on-device (PC, mobile), with more control and lower latency. They’re often very good at focused tasks, especially when the scope is clear.
Example: A customer-service bot that simply needs to answer customer questions. You don’t need a large model for that.
Quick comparison: RAG vs Fine-tuning vs SLM
If having the right information is critical, RAG is generally a great fit. RAG is designed to reference an external knowledge base rather than “remember” things through training.
If answering the right way with a solid response structure matters (e.g., answering programming questions), fine-tuning is very effective.
If response speed, cost, or privacy are essential, SLMs are strong candidates because they’re faster to train and can be deployed locally.
| Criteria | RAG | Fine-tuning | SLM |
|---|---|---|---|
| Up-to-date knowledge | Excellent | Weak (freezes what it learned) | Variable (often + RAG) |
| Style / format / “discipline” | Medium | Excellent | Good for targeted tasks |
| Hallucination risk | Reduced with good sources | Can persist | Variable (often better if scope is narrow) |
| Time to implement | Short to medium | Medium (dataset + iterations) | Medium (selection + deployment) |
| Inference cost | Medium | Can decrease depending on model | Often low |
| Maintenance | Index + doc quality | Training data + drift | Model ops + versions |
The decision matrix (simple and effective)
1) Do your documents change often?
- Yes → RAG first (otherwise you’ll retrain over and over)
- No → fine-tuning may be enough in some cases
2) Do you need citations, traceability, a “source of truth”?
- Yes → RAG (ideally with quoted passages)
- No → fine-tuning / SLM may be enough
3) Does your output need to be highly structured and stable?
- Yes → fine-tuning (or at least strict formatting constraints + tests)
- No → RAG alone can work
4) Are latency, cost, and deployment strong constraints?
- Yes → SLM (often + RAG)
- No → LLM + RAG is the fastest to ship
5) Do you have enough high-quality examples to train?
- Yes → fine-tuning becomes relevant (often with dozens/hundreds of examples depending on the case)
- No → start with RAG + prompting + evaluation, then iterate
Common mistakes to avoid
Putting evolving information into fine-tuning
Bad idea if the information changes. You pay twice: training + obsolescence. RAG is built for that.
Doing RAG without document governance
Good documentation is the foundation for RAG to work well.
Choosing an SLM without scoping the problem
A small model can be excellent, but you need:
- clearly defined tasks
- a controlled vocabulary
- automated tests
A few use cases
Customer support based on FAQs + product docs
RAG first (easy updates), then possibly fine-tuning for tone and response format.
Business assistant (HR, finance, legal) with a need for evidence
RAG + citations + access control. Fine-tuning comes next if you want to standardize outputs.
Data extraction (invoices, emails, tickets) with strict output format
Fine-tuning (or formatting rules + tests), with RAG only if you need to enrich using internal reference data.
High-volume internal copilot (cost/latency critical)
SLM + RAG. Fine-tuning is optional, useful if tasks are repetitive and measurable.
Conclusion
- RAG: best choice when knowledge must stay up to date and traceable.
- Fine-tuning: best choice to standardize behavior (format, tone, procedures), often as a complement.
- SLM: best choice to industrialize (cost, latency, deployment), especially for well-scoped tasks.