Photo by Steve Johnson on Unsplash
RAG vs Fine-Tuning: What's Better for Your AI Product MVP?
RAG vs Fine-Tuning: What's Better for Your AI Product MVP?
When building an AI product that needs custom knowledge, you have two main approaches: RAG (Retrieval-Augmented Generation) and fine-tuning. For MVPs, one is almost always the right choice.
What's the Difference?
RAG (Retrieval-Augmented Generation)
Feed relevant data to the model at query time. User asks a question → retrieve relevant documents → include them in the prompt → model generates answer based on those documents.
Fine-Tuning
Retrain the model on your specific data. The model learns your data's patterns, terminology, and style. It "knows" your data without needing it in the prompt.
Comparison
| Factor | RAG | Fine-Tuning |
|---|---|---|
| Setup time | Hours to days | Days to weeks |
| Cost | Low (vector DB + API calls) | High (training runs + hosting) |
| Data freshness | Real-time (update anytime) | Stale (requires retraining) |
| Accuracy | High for specific queries | High for patterns/style |
| Hallucination risk | Lower (grounded in retrieved docs) | Higher (can fabricate) |
| Data privacy | Data stays in your DB | Data sent to model provider |
| Best for | Q&A, document search, knowledge bases | Style matching, domain-specific language |
| MVP recommendation | Yes | Not usually |
When to Use RAG (90% of MVPs)
- Q&A over documents (legal, medical, technical)
- Customer support bots with knowledge bases
- Research assistants over proprietary data
- Product recommendation based on catalog data
- Any product where data changes frequently
RAG Implementation Stack
| Component | Tool |
|---|---|
| Embeddings | OpenAI text-embedding-3-small |
| Vector Database | Pinecone, Weaviate, or pgvector |
| LLM | GPT-4o or Claude 3.5 Sonnet |
| Framework | LangChain or Vercel AI SDK |
RAG Flow
- Index: Split documents into chunks → generate embeddings → store in vector DB
- Query: User asks question → generate query embedding → find similar chunks in vector DB
- Generate: Include top-K relevant chunks in prompt → LLM generates grounded answer
When to Use Fine-Tuning (10% of MVPs)
- Training a model to match a specific writing style
- Domain-specific terminology (medical, legal jargon)
- Consistent output formatting
- When base model consistently gets domain tasks wrong
The MVP Recommendation
Start with RAG. It's faster to implement, cheaper to run, and your data stays fresh. Fine-tune only if RAG quality isn't sufficient after optimization.
Building an AI product? Read How to Build an AI SaaS MVP.
Need a boilerplate? Browse on MVPHub.
