Key takeaways
- Clean dataset
- Regular evaluation
- Behavior versioning
- Test a simple RAG
When RAG wins
RAG is often the best choice when information changes quickly, when you need to cite sources, and when you want to iterate quickly on a document corpus.
It also helps maintain clearer governance, because quality depends largely on the corpus, chunking, retrieval, and the system prompt, which are more observable building blocks than the weights of a specialized model.
The most underestimated criterion: freshness
As soon as business content evolves frequently, RAG naturally becomes more relevant. You correct knowledge by updating the document base instead of redeploying an entire learned behavior.
When fine-tuning becomes the logical choice
Fine-tuning becomes interesting if you are looking for specialized behavior, a highly standardized output, or strong consistency across recurring patterns.
It can also be relevant if the business expects a precise style of delivery, a systematic structure, or a highly repetitive way of reasoning that goes beyond what a well-designed prompt can achieve.
The right sequence for a product team
In most cases, starting with a well-instrumented RAG remains the healthiest strategy. Fine-tuning comes later if proof of use has already been established.
This sequence protects the budget, reduces learning time, and helps the team clearly identify what comes from the corpus, prompting, UX, or a true need for model specialization.
Recommended decision order
The most robust approach is to test a simple assistant, measure the observed limitations, fix retrieval and instructions, and only then consider a heavier specialization phase.
- Test a simple RAG
- Instrument errors
- Measure repetition of gaps
- Then decide on fine-tuning
IT
Author
Imane Tahiri
LLM Engineer
