Techniques
RAG vs fine-tuning: when to use each
Retrieval vs specialized models — cost, maintenance, and quality trade-offs.
/ Our verdict
Start with RAG. Add fine-tuning only when proven necessary.
6
RAG wins
0
Ties
2
Fine-tuning wins
Side by side
How they compare, dimension by dimension.
Dimension
RAG
Fine-tuning
Time to first working version
1-2 weeks
4-8 weeks
Ongoing cost
Per-query inference + vector DB
Training + hosting fine-tuned model
Handles changing data
Re-index (hours)
Retrain (days-weeks)
Task specialization
Limited to retrieval quality
Deep specialization possible
Output style matching
Limited
Excellent
Transparency/debuggability
Sources visible
Black box
Vendor lock-in
Model-agnostic
Provider-specific
Data requirements
Unstructured docs
500+ labeled examples minimum
/ Pick RAG when
- Your data changes regularly
- You need citations and source attribution
- You have unstructured docs (wiki, PDFs, tickets)
- You want to switch models without retraining
/ Pick Fine-tuning when
- You need specific output style or format
- Task is narrow and stable
- You have 1000+ high-quality training examples
- Latency cost of long context is prohibitive
Our take
80% of problems we see are solved with RAG alone. Fine-tuning earns its place when style consistency matters (voice for brand copy) or when the task is narrow enough that specialized models outperform general ones meaningfully.
/ Next step
Still not sure which to pick?
A 30-minute call with our team is often faster than more research. Let's talk through your specific context.
~4h
avg response
Q2 '26
next slot
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NDA on request