RAG vs Fine-Tuning
When to retrieve, when to retrain.
/ 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.
| 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 |
- →Customer support over a changing knowledge base → RAG
- →Consistent brand voice across all generations → Fine-tuning
/ 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.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
- →AI Agent
An autonomous AI system that plans and executes multi-step tasks.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
- →Prompt Chaining
Pipelining LLM calls where each step's output feeds the next.
Related workflows
Turn this into a repeatable process.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →AI Customer Onboarding Flow
Walk every new user through activation with an AI guide.
- →Automated Competitor Research
From a product description to a structured competitor matrix in under 10 minutes.
- →AI Content Factory: One Topic to Ten Assets
Convert a single topic into a full multi-channel content drop.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →Agent Research Stack
Web-search-enabled agent for autonomous research tasks.
- →Solo Content Creator Stack
End-to-end AI stack for one operator running a multi-channel content engine.
- →AI SDR & Outbound Sales Stack
Stack that runs lead enrichment, scoring, and personalized outreach end-to-end.
Related prompts
Reusable prompts for this job.
- →Competitor Discovery Prompt
Surface and structure direct competitors for a given product.
- →Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
- →Viral Hook Generator Prompt
Produce 10 scroll-stopping hooks for a topic and platform.
- →Cold Email Sequence Prompt
Draft a 3-touch personalized outbound sequence per lead.