RAG in production
Indexing, re-ranking, citations and answer evaluation over your real documents.
Fine-tuning & 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
There's no magic recipe — there are constraints. 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.
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
0fixed prompts per evaluation
0languages evaluated
0routes compared: RAG & adapters
0%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.
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→ RAGFine-tuning vs RAG
When RAG, when fine-tune, when hybrid.
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 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.