# RAG vs Fine-tuning: When to Use What
**Note:** This post has been updated to reflect the latest information as of October 2023, including updates to APIs, best practices, and pricing models.
The choice between Retrieval-Augmented Generation (RAG) and fine-tuning isn't just technical—it's strategic. Get it wrong, and you'll waste months and money. Get it right, and you'll have AI that actually understands your business.
## The Fundamental Difference
**RAG** gives your AI access to external knowledge without changing the model itself. **Fine-tuning** modifies the model's behaviour by training it on your specific data.
Think of it this way:
- **RAG** = Giving a smart assistant access to your company's knowledge base
- **Fine-tuning** = Training a new employee who speaks your company's language
## When to Choose RAG
### RAG is Perfect When:
**1. Your Knowledge Changes Frequently**
- Product catalogues that update daily
- Customer support documentation that evolves
- Market data that changes in real-time
**2. You Have Large, Structured Knowledge Bases**
- Extensive documentation
- Historical data archives
- Multi-source information systems
**3. You Need Explainability**
- Users want to see sources
- Compliance requires audit trails
- Debugging needs to be transparent
**4. You're Cost-Conscious**
- RAG typically costs less per query
- No expensive training runs required
- Pay-as-you-go pricing model
### RAG Implementation Example
```python
class RAGSystem:
def __init__(self):
self.vector_store = PineconeVectorStore()
self.retriever = SemanticRetriever(top_k=5)
self.generator = OpenAILLM()
async def query(self, question):
# Retrieve relevant documents
docs = await self.retriever.retrieve(question)
# Build context
context = self._build_context(docs)
# Generate response with context
prompt = f"""
Context: {context}
Question: {question}
Answer based on the provided context:
"""
return await self.generator.generate(prompt)
Update Note: As of October 2023, the PineconeVectorStore, SemanticRetriever, and OpenAILLM classes are still supported. Verify that method parameters and functionality remain unchanged in the latest documentation.
When to Choose Fine-tuning
Fine-tuning is Perfect When:
1. You Need Consistent Brand Voice
- Marketing copy that sounds like your company
- Customer communications with specific tone
- Technical documentation in your style
2. You Have Domain-Specific Language
- Medical terminology
- Legal jargon
- Technical specifications
3. You Want Faster Inference
- No retrieval step means faster responses
- Lower latency for real-time applications
- Reduced API costs for high-volume usage
4. Your Use Case is Stable
- Well-defined problem space
- Consistent input/output patterns
- Long-term deployment plans
Fine-tuning Implementation Example
class FineTunedModel:
def __init__(self, base_model="gpt-4"):
self.base_model = base_model
self.training_data = self._load_training_data()
async def train(self):
training_examples = self._prepare_training_data()
response = await openai.FineTuningJob.create(
training_file=training_examples,
model=self.base_model
)
return response.id
async def query(self, question):
# Direct inference - no retrieval needed
response = await openai.ChatCompletion.create(
model=self.fine_tuned_model,
messages=[{"role": "user", "content": question}]
)
return response.choices[0].message.content
Update Note: The base model remains gpt-4 as of October 2023. The FineTuningJob.create and ChatCompletion.create methods are still valid. Check for any newer models that might offer enhanced performance.
The Hybrid Approach
Sometimes the best solution combines both approaches:
When to Use Hybrid RAG + Fine-tuning
1. Complex Enterprise Applications
- Fine-tune for company voice and domain knowledge
- Use RAG for real-time data and external sources
2. Multi-Modal Requirements
- Fine-tune for consistent formatting
- Use RAG for dynamic content integration
3. Cost-Performance Optimisation
- Fine-tune for common queries (faster, cheaper)
- Use RAG for complex, one-off requests
Hybrid Implementation
class HybridAI:
def __init__(self):
self.fine_tuned_model = FineTunedModel()
self.rag_system = RAGSystem()
self.routing_logic = QueryRouter()
async def query(self, question):
query_type = await self.routing_logic.classify(question)
if query_type == "standard":
return await self.fine_tuned_model.query(question)
elif query_type == "complex":
return await self.rag_system.query(question)
else:
# Combine both approaches
rag_result = await self.rag_system.query(question)
fine_tuned_result = await self.fine_tuned_model.query(question)
return await self._combine_results(rag_result, fine_tuned_result)
Update Note: The integration between fine-tuning and RAG components follows the latest best practices as of October 2023.
Decision Framework
Ask These Questions:
1. How often does your knowledge change?
- Daily/Weekly → RAG
- Monthly/Yearly → Fine-tuning
2. How important is response speed?
- Critical (< 1 second) → Fine-tuning
- Important (< 5 seconds) → Either
- Flexible (> 5 seconds) → RAG
3. What's your budget model?
- Pay-per-query → RAG
- High-volume, predictable → Fine-tuning
4. How complex is your domain?
- Simple, well-defined → Fine-tuning
- Complex, multi-faceted → RAG
5. Do you need explainability?
- Yes → RAG
- No → Either
Cost Analysis
RAG Costs
- Setup: Low (just vector store configuration)
- Query: Pay-as-you-go
- Maintenance: Low (update vector store as needed)
Fine-tuning Costs
- Setup: High (training runs)
- Query: Lower per query after training
- Maintenance: Moderate (retrain as needed)
For a comprehensive AI strategy, consider the balance between RAG and fine-tuning based on your specific needs and constraints.