RAG vs Fine-Tuning: When to Use Each

"Should we use RAG or fine-tune a model?" We hear this question constantly. The answer is almost always: both, for different purposes.

RAG and fine-tuning solve different problems. Understanding when to use each - and how they complement each other - is fundamental to enterprise AI architecture.

What RAG Does Well

Retrieval-Augmented Generation excels at bringing external knowledge into the model's context at inference time. Use RAG when:

• Knowledge changes frequently - Policy updates, new procedures, recent documents
• You need citations - Compliance requires showing source documents
• Facts matter more than style - Lookup and retrieval tasks
• You have limited training data - Can work with just the documents themselves

flowchart LR
    Query[User Query] --> Embed[Embed Query]
    Embed --> Search[Vector Search]
    Docs[(Document Store)] --> Search
    Search --> Context[Retrieved Context]
    Context --> LLM[Base LLM]
    Query --> LLM
    LLM --> Response[Grounded Response]
                

RAG Limitations

• Can't change how the model reasons or responds
• Retrieval quality limits response quality
• No learning of domain-specific patterns
• Same response style regardless of domain

What Fine-Tuning Does Well

Fine-tuning modifies the model's weights to change its behavior. Use fine-tuning when:

• Response format matters - Specific structures, templates, styles
• Domain terminology is specialized - Medical, legal, technical jargon
• Reasoning patterns are domain-specific - How to analyze, what to prioritize
• Consistency is critical - Same format every time

flowchart LR
    TrainData[(Training Data)] --> FineTune[Fine-Tuning Process]
    BaseModel[Base Model] --> FineTune
    FineTune --> DomainModel[Domain Model]
    Query[User Query] --> DomainModel
    DomainModel --> Response[Domain-Styled Response]
                

Fine-Tuning Limitations

• Requires substantial training data (500+ examples)
• Knowledge is frozen at training time
• Can't cite sources for claims
• Risk of catastrophic forgetting

The Hybrid Approach

In production systems, we use both. RAG provides the facts. Fine-tuning provides the expertise in how to use those facts.

flowchart TB
    Query[User Query] --> Retrieval[RAG Retrieval]
    Docs[(Documents)] --> Retrieval
    Retrieval --> Context[Retrieved Facts]
    
    Query --> Domain[Domain-Tuned Model]
    Context --> Domain
    
    Domain --> Response[Expert Response with Citations]
                

Example: Insurance Claims

Practical Decision Framework

Use RAG for what the model should know. Use fine-tuning for how the model should think.

When evaluating a capability, ask:

1. Does this change over time? → RAG
2. Do we need to cite sources? → RAG
3. Is this about format or style? → Fine-tuning
4. Is this domain-specific reasoning? → Fine-tuning
5. Is this factual lookup? → RAG
6. Is this pattern recognition? → Fine-tuning

LoRA: Making Fine-Tuning Practical

Traditional fine-tuning is expensive and creates model management headaches. LoRA (Low-Rank Adaptation) changes this by training small adapter layers instead of full model weights.

• Efficient: Train in hours, not days
• Composable: Swap adapters at inference time
• Manageable: Adapters are MBs, not GBs
• Safe: Base model unchanged, easy rollback

This enables our domain routing architecture - different LoRA adapters for different domains, all on the same base model, swapped dynamically based on query classification.