Two approaches to adapt generative AI to your context. Which one should you choose based on your needs?
Two Paths to Adapt AI to Your Reality
Generative artificial intelligence is no longer reserved for labs: it's entering businesses. But to make it useful, you need to adapt it to your context, your data, your language.
Two approaches dominate: RAG (Retrieval Augmented Generation) and Fine-Tuning. Both aim for the same goal—obtaining relevant answers—but their logics are radically different.
Fine-Tuning: Teaching the Model
Fine-Tuning consists of retraining an existing model (like GPT or Mistral) with your own data. The idea: make it "learn" the specifics of your domain, your jargon, your business rules. The model thus becomes a customized version, more adapted to your environment.
It's like training an already skilled employee in your way of working: they keep their general knowledge but acquire your internal culture.
Advantages
- Very precise results on repetitive and well-defined tasks.
- Homogeneous and consistent responses.
- Works even without access to an external database.
Limitations
- High cost (retraining + GPU infrastructure).
- Continuous maintenance (relearning with each change).
- Risk of "overfitting" if data is limited or biased.
Fine-Tuning is ideal when you control your data and it evolves slowly.
RAG: Connect Rather Than Retrain
Retrieval Augmented Generation (RAG) takes another approach: instead of modifying the model, we give it access to external memory.
Concretely, we connect the model to a vectorized document base (your documents, procedures, emails, etc.), and with each question, it searches for the right information before generating a contextualized response.
The model remains unchanged, but its answers become relevant to your business.
Advantages
- No model modification (therefore simpler to maintain).
- Data always up to date.
- Reduced risk of bias or information loss.
Limitations
- Requires good data preparation (indexing, cleaning).
- Depends on the quality of document retrieval.
- Less effective on creative tasks requiring specific style or tone.
RAG is ideal for dynamic environments where information evolves rapidly.
RAG or Fine-Tuning? The False Dilemma
Rather than opposing the two, it's often smarter to combine them. For example, you can fine-tune a model on general business language, then use RAG for recent contextual information.
Example:
An insurance company can fine-tune a model to understand its vocabulary (contract, claim, endorsement), while connecting a RAG to client documents and local rules to generate personalized responses.
This duo allows combining the depth of knowledge (Fine-Tuning) with the freshness of data (RAG).
The Right Choice Depends on Your Maturity
Organizations mature in data governance will benefit more from Fine-Tuning. Those looking to start quickly and with agility will find in RAG an excellent entry point.
Stable and internal data → Fine-Tuning
Dynamic and external data → RAG
Large budget, critical use → Fine-Tuning
Rapid implementation → RAG
Need for strong control → Both
At Ti Ael Mat: Guide the Right Choice, Not Impose Technology
There is no best AI, only the best AI for your context.
Our role is not to sell a solution, but to design the right AI architecture for your needs:
Mapping available data.
Choosing appropriate models.
Defining the approach (RAG, Fine-Tuning, or hybrid).
Governance and human support.
Because beyond technical performance, it's the consistency between data, model, and mission that creates value.
In Summary
RAG and Fine-Tuning are not two opposing camps, but two complementary levers to build an AI that understands and learns from your business.
One connects knowledge, the other incorporates it. And the most advanced companies are those that know how to play with both.
AI is not just about models. It's an art of balance between knowledge, memory, and meaning.