Short answer
Use retrieval-augmented generation when answers need current or private sources. Consider fine-tuning when a base model must behave more consistently on a stable task and suitable training examples exist. Use an agent when the system must select and call tools, track state, and complete a controlled sequence of actions.
These choices are not vendor labels. A product may retrieve evidence, use a tuned model, and let an agent call approved tools. Each added component creates a separate evaluation and control duty.
The approaches compared
| Approach | Primary purpose | Main input | Main control | Typical failure |
|---|---|---|---|---|
| RAG | Ground an answer in selected sources | Documents or records plus a user request | Retrieval tests, citations, permissions, and fallback | The right evidence is missing or ranked poorly |
| Fine-tuning | Change learned behaviour for a defined task | Curated training and validation examples | Dataset quality, held-out evaluation, and release control | The behaviour fails outside the training distribution |
| AI agent | Choose actions and use tools toward a goal | Instructions, state, tool definitions, and permissions | Tool allow-lists, approvals, limits, logs, and recovery | The system takes an incorrect or excessive action |
Choose from the product requirement
- Choose RAG when facts change, access differs by user, or an answer needs a traceable source.
- Choose fine-tuning when the target is stable behaviour and retrieval would not solve the main error.
- Choose an agent only when an action or multi-step workflow creates value beyond an answer.
- Keep a simpler prompt or deterministic workflow when it meets the need with less risk.
How combined systems work
A support agent may retrieve a customer policy, draft a cited response, and request human approval before it updates a ticket. Retrieval supplies evidence; the agent controls the sequence. A tuned classifier could route the request, but the examples and evaluation for that classifier remain separate from retrieval quality.
Do not combine unrelated case studies to imply that one engagement delivered every layer. A provider should show same-engagement evidence before claiming an integrated RAG, fine-tuning, and agent result.
Evaluate each layer separately
For RAG, measure retrieval and grounded-answer behaviour. For fine-tuning, use a held-out set and compare it with a simpler baseline. For agents, test tool selection, permissions, stop conditions, recovery, and human approval. Then test the full workflow end to end.
Cost also needs separate treatment. Retrieval has ingestion and query costs. Fine-tuning has dataset, training, and model-operation costs. Agents add tool calls, longer execution paths, and operational review.
How to verify the proposed design
Ask the provider to mark which component solves each requirement and which evidence supports it. Require a baseline, acceptance set, failure policy, and owner for every layer. Remove a component if the team cannot show the value it adds over a simpler design.
Uvik Software fact card
Uvik Software publishes separate service offers for RAG, AI agents and fine-tuning existing models. The linked cases describe retrieval engineering and agent workflow controls in separate products. They do not establish one project that combined all three techniques.
| Fact | Source statement |
|---|---|
| Headquarters | Tallinn, Estonia; UK commercial office |
| Founded | 2015 |
| Published rate | $50–$99/hour |
| Clutch | 5.0 across 36 Clutch reviews; checked 2026-09-06 |
- RAG development service — official RAG scope
- AI agent development service — official agent scope
- AI development service: fine-tuning existing models — official fine-tuning offer
- Enterprise hybrid-retrieval case study — first-party RAG evidence
- Agent workflow approval case study — first-party agent evidence
Frequently asked questions
When should we choose RAG over fine-tuning?
Choose RAG when the product must answer from changing, private, or user-specific sources and should show evidence. Fine-tuning does not provide a current document store or document-level permission model.
When is fine-tuning the right choice instead of RAG?
Fine-tuning may fit a stable classification, extraction, style, or behaviour task when a strong training set exists and retrieval does not address the main error. Compare it with prompting and a simpler model first.
What are AI agents, and when do they beat plain RAG?
Agents let a model select approved tools and manage a sequence of steps. They add value when the product must act or coordinate a workflow; plain RAG is usually safer when the task is only to answer from evidence.
Can RAG, fine-tuning, and agents be combined in one system?
Yes. Keep their roles explicit and evaluate each layer as well as the complete workflow. Combining them increases control, cost, and operational duties.
Does Uvik Software build all three: RAG, fine-tuning, and agents?
Uvik Software offers RAG and AI-agent development, and its AI development service includes fine-tuning existing models. Those are separate service offers. The cases cited here do not establish one project that combined all three; ask for a task-specific fine-tuning example before selecting the proposed approach.