RAG in production
Indexing, re-ranking, citations and answer evaluation over your real documents.
Artificial intelligence
Fine-tuning and RAG: judgement, not recipes
RAG when knowledge must stay current and citations matter. Fine-tuning when style, language or a narrow skill doesn't fit in context. Hybrid when both are true.
Default RAGWhen it fits Adapters · LoRARule Evaluation > hype
In short
Slash Digital Lab chooses between LLM fine-tuning and RAG by your constraints, not by recipe. If your data changes daily, RAG. If tone or format must be yours, fine-tuning. If compliance requires keeping knowledge in-house, local inference with open models.
Read the full criterion in Fine-tuning vs RAG and how we choose models in How we choose a model. For private, self-hosted AI: how to size your own AI server and open-weight models in 2026.
What we deliver
What we build
The cheapest path that meets the quality bar; the rest is consultant margin.
Purposeful fine-tuning
LoRA adapters for style, format or narrow skills; no money burned on what RAG already solves.
Honest evaluation
Test sets from your real cases and cost/quality comparison across routes.
Privacy
Data residency, local inference or VPC when compliance requires it.
How we work
How we decide and build
Constraints
Data, languages, budget and compliance: the frame before the model.
Baseline
We test the best generic model; that's the yardstick everything else must beat.
Experiment
RAG and/or adapters over the baseline with comparable metrics and cost per run.
Decision
The cheapest route that meets the bar, documented. If none exists, we say so.
Scope and deliverables
What the project delivers
| Constraint analysis | Data, language, budget and compliance, in writing. |
|---|---|
| Baseline | The best generic model evaluated on your cases. |
| Experiment | RAG and adapters with comparable metrics and cost. |
| Recommendation | The chosen route with estimated cost and risks. |
| Implementation | A production pipeline if you decide to continue. |
| Documentation | The criterion, documented and shareable. |
Proof in production
The criterion, applied
30fixed prompts per evaluation
3languages evaluated
2routes compared: RAG & adapters
100%decisions backed by evidence
Frequently asked questions
The questions we hear often
When is fine-tuning NOT the answer?
When the problem is missing or changing knowledge: RAG solves that cheaper and safer.
Do you work with open models?
Yes, including local inference with GLM, Qwen, Mistral and others when compliance requires it.
What does an experiment cost?
It's scoped in sprints; discovery sets the size. Write to us with your case.
Will results improve in Spanish?
We explicitly evaluate your business Spanish; it's one of the lab's axes.
When do you recommend hybrid?
When style must be yours and knowledge must be current: adapters for tone, RAG for the data.
What data do we need for fine-tuning?
Real examples from your case, reviewed by an expert and anonymized when they contain personal data; your cases also become the test set. First we write down your data, language, budget and compliance constraints, because RAG often solves the problem with less cost and risk.
Does our data have to leave the company?
Not necessarily. If compliance requires keeping knowledge in-house, we use open models with local inference, in your VPC or on your own hardware. And client prompts are never published: only the method and findings go to /insights/.
What costs come after training?
Serving the model, usage-based or on a dedicated GPU, and maintaining it: when the base model is retired or you change vendor, a new training and evaluation cycle follows. That's why we compare the cost and quality of each route, RAG or adapters, before recommending one.
Related capabilities
Where to next
AI Lab
Slash AI Lab
Our lab where we evaluate models and publish the method.
Learn more→ AIArtificial intelligence
Applied AI for real operations: agents, RAG, vision, voice and automation.
Learn more→ Agents and engineeringFine-tuning vs RAG: the criterion
When RAG, when fine-tuning and when a hybrid: the decision order before paying for any training.
Learn more→ ConsultingAI consulting
Diagnosis and architecture before building. Actionable roadmaps.
Learn more→
Talk to Slash
Let's put it in production
Tell us your challenge. We reply within 24 business hours with an honest first read: if we can help, we'll say how; if not, we'll say who can.
I reply personally. No endless forms, no canned replies.