RAG vs Fine-tuning

Two approaches dominate knowledge enhancement for LLMs: RAG and fine-tuning. Understanding when to use each is crucial for production systems.

Fundamental Differences

Aspect	RAG	Fine-tuning
Knowledge source	External retrieval	Embedded in weights
Update frequency	Real-time possible	Requires retraining
Cost structure	Per-query retrieval	Upfront training cost
Hallucination control	Grounded in sources	Still possible
Domain adaptation	Works immediately	Needs training data

When to Choose RAG

RAG excels when:

1. Knowledge changes frequently

# RAG: Update documents, immediately available
def add_new_knowledge(document: str):
    chunks = chunker.split(document)
    embeddings = embed_model.encode(chunks)
    vectorstore.add(chunks, embeddings)
    # Immediately queryable - no retraining

2. You need source attribution

# RAG provides traceable answers
response = {
    "answer": "The policy allows 30-day returns.",
    "sources": [
        {"doc": "return_policy.pdf", "page": 3},
        {"doc": "faq.md", "section": "returns"}
    ]
}

3. Knowledge base is large or diverse

Legal documents across jurisdictions
Technical documentation spanning products
Historical data with temporal context

4. Accuracy is critical

Medical information retrieval
Financial compliance queries
Legal research applications

When to Choose Fine-tuning

Fine-tuning excels when:

1. Consistent style/format needed

# Fine-tuned model learns your voice
# Training data:
# Input: "Summarize this email"
# Output: "[Company style summary format]"

2. Task-specific behavior

Custom classification schemas
Domain-specific entity extraction
Proprietary reasoning patterns

3. Latency is critical

No retrieval overhead
Single model inference
Predictable response times

4. Knowledge is stable

Core domain concepts don't change
Training data represents complete knowledge

The Hybrid Approach

Production systems often combine both:

class HybridKnowledgeSystem:
    def __init__(self):
        # Fine-tuned for domain understanding
        self.llm = load_fine_tuned_model("domain-expert-v2")
        # RAG for current facts
        self.retriever = VectorRetriever("knowledge_base")

    def answer(self, query: str):
        # Retrieve current facts
        context = self.retriever.search(query)

        # Fine-tuned model understands domain nuances
        return self.llm.generate(
            query=query,
            context=context,
            # Model knows domain terminology, style
        )

Cost Comparison

Factor	RAG	Fine-tuning
Initial setup	Index creation (~$0.10/1M tokens)	Training (~$10-100+ per run)
Per-query cost	Embedding + retrieval + generation	Generation only
Update cost	Re-embed changed docs	Full retraining
Infrastructure	Vector DB hosting	Model hosting

Decision Framework

START
  │
  ▼
Does knowledge change frequently?
  │
  ├─ YES → RAG
  │
  ▼
Need source attribution?
  │
  ├─ YES → RAG
  │
  ▼
Is consistent style critical?
  │
  ├─ YES → Fine-tuning (+ optional RAG)
  │
  ▼
Latency under 100ms required?
  │
  ├─ YES → Fine-tuning
  │
  ▼
Default → RAG (more flexible, easier to update)

Production Reality: Most enterprise applications start with RAG because it's faster to deploy, easier to update, and provides the auditability that compliance requires.