RAG vs Fine-Tuning: Choosing the Right Approach for Your Agent

When building AI agents that need domain-specific knowledge, developers face a fundamental choice: retrieval-augmented generation (RAG) or fine-tuning. Both approaches have distinct strengths and trade-offs. Understanding when to use each one is critical for building effective agents.

Understanding RAG

RAG works by retrieving relevant documents from a knowledge base at query time and including them in the agent’s context window. The agent’s base knowledge remains unchanged — it simply gets better context for each specific query. This approach is ideal when your knowledge base changes frequently or contains sensitive data you cannot include in training.

Understanding Fine-Tuning

Fine-tuning permanently modifies the model’s weights using your domain-specific data. The resulting model inherently “knows” your domain without needing retrieval at inference time. This approach excels when you need consistent formatting, domain-specific reasoning patterns, or faster response times without retrieval overhead.

When to Choose RAG

• Knowledge that updates frequently (daily, weekly)
• Large knowledge bases (millions of documents)
• Need for source attribution and traceability
• Multi-tenant systems where each customer has different data
• Budget constraints (no training compute costs)

When to Choose Fine-Tuning

• Stable domain knowledge that rarely changes
• Need for specific output formats or tone
• Latency-critical applications where retrieval adds overhead
• Small, focused knowledge domains
• Teaching the model new reasoning patterns

The Hybrid Approach

In practice, the best agents combine both techniques. Fine-tune for domain-specific reasoning and output style, then use RAG for factual grounding and real-time information. This hybrid approach gives you the best of both worlds: an agent that thinks like a domain expert while having access to the latest information.