Key Takeaways
- RAG retrieves your documents at answer time; fine-tuning changes model weights. Most UK enterprises should start with RAG.
- Fine-tuning costs more to prepare, evaluate, and retrain when policies change — RAG updates when documents update.
- Choose fine-tuning when you need consistent style or specialised behaviour at very high volume and have labelled data.
- Zeviq AI implements production RAG and selective fine-tuning as part of LLM and GPT integration.
- Budget UK projects in £ GBP including retrieval infra, evaluation, and ongoing maintenance — not just API tokens.
Table of Contents
- What Is RAG?
- What Is Fine-Tuning?
- RAG vs Fine-Tuning Comparison (UK £)
- Decision Framework for UK Enterprises
- How Zeviq AI Delivers RAG Projects
- Frequently Asked Questions
What Is RAG?
Retrieval-Augmented Generation (RAG) means the model looks up relevant passages from your knowledge base (policies, course materials, SOPs) before answering. The model stays general; your documents supply the facts.
RAG is the default for UK colleges, care providers, and SMEs that need answers grounded in current internal content without retraining a model every time a handbook changes.
What Is Fine-Tuning?
Watch ZevIQ AI’s “What is AI Fine-Tuning? It's Simpler Than You Think” video on this page for a plain-English walkthrough of weight updates vs RAG.
Fine-tuning continues training a base model on your examples so it internalises patterns — tone, format, domain phrasing, or specialised classification. Facts can still drift; fine-tuning is better at behaviour than at staying current with a living document set.
RAG vs Fine-Tuning Comparison (UK £)
| Criterion | RAG | Fine-Tuning |
|---|---|---|
| What changes | Documents + retrieval index | Model weights |
| Typical UK project cost | £4,000 – £20,000 for a focused knowledge assistant | £15,000 – £60,000+ including data prep and eval |
| Ongoing cost | Hosting/embeddings + token usage | Periodic retrain + hosting specialised model |
| Latency | Slightly higher (retrieve then generate) | Often lower at inference if no retrieval step |
| Factual currency | Updates when docs update | Stale until you retrain |
| Accuracy on private facts | Strong when retrieval is good | Weak unless facts were in training data |
| Best for | Policies, LMS content, support knowledge | Style, classification, specialised formats at scale |
| GDPR / data control | Clear corpus scoping and UK/EU hosting | Training data governance + model hosting choices |
Watch the ZevIQ AI “RAG vs Fine-Tuning” video above for a concise walkthrough of the trade-offs.
Decision Framework for UK Enterprises
Choose RAG when:
- Answers must cite current handbooks, course guides, or SOPs
- Content changes monthly or weekly
- You need auditability (“which document supported this answer?”)
- Budget and timeline favour an MVP in weeks
Choose fine-tuning when:
- You have thousands of labelled examples
- Output format/style consistency matters more than live document lookup
- Volume is high enough that a specialised model pays back
- You have ML ops capacity (or a partner) for evaluation and redeploy
Use both when:
- Fine-tuning sets behaviour/format and RAG supplies facts — common in mature stacks, higher complexity.
How Zeviq AI Delivers RAG Projects
Under LLM and GPT integration we typically:
- Inventory and clean source documents
- Chunk and embed with a retrieval evaluation set
- Add guardrails, citations, and access controls
- Deploy on UK/EU-friendly infrastructure where required
- Monitor failure modes (empty retrieval, conflicting docs)
Related: What is an AI agent? and MCP for business systems.
Frequently Asked Questions
Should my UK business use RAG or fine-tuning first?
Almost always RAG first. It is cheaper to keep current, easier to audit, and sufficient for most internal Q&A and support use cases.
How much does a RAG project cost in the UK?
Focused assistants often fall in the £4,000–£20,000 band depending on corpus size, integrations, and evaluation depth. Ongoing costs are hosting, embeddings, and LLM tokens.
Does fine-tuning replace the need for company documents in the prompt?
No. Fine-tuning does not reliably store all private facts. If documents change, RAG or tool lookups remain the safer pattern.
Can Zeviq AI do both RAG and fine-tuning?
Yes. We recommend RAG (and tools/MCP where needed) for most builds, and fine-tuning only when evaluation shows a clear ROI.
Is RAG better for GDPR?
RAG makes it easier to scope which documents enter the context and to host the corpus in the UK/EU. Fine-tuning requires careful training-data governance as well.