Comparison
RAG vs Fine-Tuning
When to retrieve, when to retrain.
1 min read
/ quick answer
Both techniques specialize an LLM for a domain, but they optimize for different constraints. RAG injects fresh knowledge at query time; fine-tuning bakes patterns into model weights. When to retrieve, when to retrain.
When to retrieve, when to retrain. Both techniques specialize an LLM for a domain, but they optimize for different constraints. RAG injects fresh knowledge at query time; fine-tuning bakes patterns into model weights. Recommendation: Start with RAG. Only fine-tune once you have evidence that prompt + retrieval cannot produce the behavior you need. This comparison node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Overview
Both techniques specialize an LLM for a domain, but they optimize for different constraints. RAG injects fresh knowledge at query time; fine-tuning bakes patterns into model weights.
Differences
| Dimension | Option A | Option B |
|---|---|---|
| Freshness | Updated by re-indexing | Stale until retrained |
| Cost | Low ongoing | High per training run |
| Best for | Facts, docs, citations | Tone, format, style |
| Setup time | Hours | Days |
Use Cases
- →Customer support over a changing knowledge base → RAG
- →Consistent brand voice across all generations → Fine-tuning
Recommendation
Start with RAG. Only fine-tune once you have evidence that prompt + retrieval cannot produce the behavior you need.
Related Workflows
Related Tool Stacks
/ frequently asked
What is the difference in RAG vs Fine-Tuning?
Both techniques specialize an LLM for a domain, but they optimize for different constraints. RAG injects fresh knowledge at query time; fine-tuning bakes patterns into model weights.
What are the main points of comparison?
Freshness: Updated by re-indexing vs Stale until retrained · Cost: Low ongoing vs High per training run · Best for: Facts, docs, citations vs Tone, format, style · Setup time: Hours vs Days
Which one should I choose?
Start with RAG. Only fine-tune once you have evidence that prompt + retrieval cannot produce the behavior you need.
↳ connected nodes
Workflow↳ linked
Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
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AI Customer Onboarding Flow
Walk every new user through activation with an AI guide.
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RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
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Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
Dictionary↳ linked
AI Agent
An autonomous AI system that plans and executes multi-step tasks.
Dictionary↳ linked
RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.