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RAG vs Fine-Tuning: Which Approach is Right for Your AI Agent?

April 22, 2026 • 6 min read

When building an AI agent that knows about your business, you have two main approaches: RAG (Retrieval-Augmented Generation) and fine-tuning. Here's how to choose.

What is RAG?

RAG works by storing your documents in a vector database. When a customer asks a question, the system finds relevant chunks of text and includes them in the prompt to the AI model.

Think of it like giving the AI a reference book it can look up before answering.

What is Fine-Tuning?

Fine-tuning trains the AI model itself on your data. The knowledge becomes "baked into" the model's weights, so it can answer without looking anything up.

Think of it like teaching someone your business so well they memorize everything.

When to Use RAG

  • Frequently changing information — product catalogs, pricing, availability
  • Large knowledge bases — hundreds or thousands of documents
  • Need for citations — you want to show where answers came from
  • Quick deployment — RAG works immediately after uploading documents
  • Cost sensitivity — no expensive training runs required

When to Use Fine-Tuning

  • Specific tone or style — you want the AI to sound exactly like your brand
  • Complex reasoning patterns — domain-specific logic that's hard to explain in documents
  • Static knowledge — information that rarely changes
  • Latency requirements — fine-tuned models don't need retrieval lookups

The Hybrid Approach

Many production systems use both. Fine-tune a model for your brand voice and reasoning style, then use RAG for factual information that changes.

For example: fine-tune for how to handle complaints empathetically, but use RAG for current product availability.

Our Recommendation

For most customer service use cases, start with RAG. It's faster to set up, easier to update, and works well for 90% of scenarios.

Consider fine-tuning only if you have specific requirements that RAG can't meet, and you have the resources to maintain a fine-tuned model.

How Indexu Handles This

Indexu uses RAG by default with support for multiple vector stores (pgvector, Pinecone, Qdrant, and more). You can:

  • Upload documents in any format
  • Crawl websites automatically
  • Use any embedding provider
  • Update knowledge instantly without retraining

Want to see RAG in action?
Request a demo and we'll show you how to build a knowledge-powered AI agent.