Use RAG for changing sources
Documents, contracts, policies, and records update without retraining the model.
Decision / RAG versus fine-tuning
RAG changes what the model reads. Fine-tuning changes how it writes.
RAG grounds answers in your current documents and citations. Fine-tuning adjusts the model's style, format, or behavior on a fixed training set. Most business workflows start with RAG and add fine-tuning only when output behavior needs to change.
Current knowledgeSource citationsFormat and toneMaintenance cost
Pattern demonstration / same workflow, synthetic material
Fixed-scope setupClient-owned accountsDraft-and-review firstFull handover
How to choose
The question is what needs to change.
If the problem is stale or missing knowledge, retrieval fixes it without retraining. If the problem is that answers are not in the right structure, fine-tuning may help.
Consider tuning for fixed style
Fine-tuning can shape tone, structure, and response format when examples are stable.
Compare maintenance
RAG is easier to refresh; fine-tuning adds training, versioning, and evaluation work.
The model decision boundary
Neither method removes human review.
RAG cites what it knows
Answers can point to the source when retrieval is the foundation.
Fine-tuning is not memory
Tuned models still need sources and evaluation for current facts.
Outputs stay reviewed
High-stakes outputs keep a named reviewer and approval step.
A first model decision
Start with the failure you are fixing.
Describe the failure
Is the answer missing knowledge, poorly structured, or inconsistent?
Try retrieval first
Ground the model in the current documents and evaluate the result.
Test format changes
Use prompts and structured outputs before committing to fine-tuning.
Decide with evidence
Compare the evaluation results, maintenance cost, and review burden.
Choose the method that fixes the observed failure, not the method that sounds more advanced.
Why trust Pristine3D?
We build and operate production software.
Pristine3D Ltd builds and operates live digital products, and we run private AI workflows internally as part of our own operations. We scope around your real workflow: the documents you own, the questions your team asks, and the access boundary you approve. Based in Lagos, Nigeria, we work remotely with clients worldwide.
We already run live products
Based in Lagos, Nigeria, Pristine3D currently builds and operates smartcards.ng, venu.ng, photoshoot.ng, and ugc.ng in production.
We start with the actual workflow
One input, one output, one test set, and one person who owns the result. We scope a real workflow instead of a transformation programme.
The boundary stays visible
Cloud, model, storage, and messaging accounts stay in your name. The chosen data path, access rules, test record, documentation, and training are part of the agreed scope.
Pricing / fixed scope
Know the starting numbers before you ask.
The final quote follows the workflow. Infrastructure and model bills stay on your accounts.
One Workflow
Starting from$3,500 / ₦2.5m
2 to 3 weeksOne workflow for a small team, with training and handover.
See the one-workflow packageFirm-wide
Starting from$7,500 / ₦8m
4 to 6 weeksMultiple sources, roles, integrations, and admin handover.
See the deploymentAnnual support
Starting from$3,000 / ₦1.5m
per yearStandard care for one delivered workflow. Optional. Larger deployments and active monitoring are separately scoped.
See supportArchitecture review from $500. Standard annual support is $3,000 / ₦1.5m per year for one delivered workflow. New workflows, integrations, active monitoring, and infrastructure are separately scoped; infrastructure, model, storage, and messaging bills stay on client accounts. Full pricing and what changes the quote
Straight answers
Common questions.
Do most businesses need fine-tuning?
No. Most first workflows improve with better retrieval, prompts, and evaluation. Fine-tuning is reserved for cases where behavior itself must change.
Can we use both?
Yes. RAG can feed current knowledge to a tuned model when the combination is justified.
Who evaluates the choice?
We test both routes against your real questions and show the difference before you pay for more.
Own your knowledge base
The model is not the product. The knowledge base is.
Documents, the retrieval index, access rules, and the workflows built around them are the asset, and they compound. We deploy so the knowledge base stays yours: on your accounts, in the environment you choose, under access rules your team defines. The model behind the answers is a connector, so the knowledge base moves with you, not with a vendor.
Your corpus, your index
The document store, metadata, and retrieval setup live on accounts you own. No vendor holds the corpus.
Models are swappable parts
Change the model provider, move regions, or go local without rebuilding the knowledge base or the workflow.
The knowledge base is the alpha
Every improvement to the corpus improves the answers, and the improvement stays with you, not with a vendor.
Keep exploring
Related setups.
Start with the failure
What is wrong with the current output?
Tell us the task, the documents, and the output problems. We will recommend RAG, tuning, or neither based on tests.