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

You want an LLM that knows your business. But how should you customise it?

Two main approaches dominate: Retrieval Augmented Generation (RAG) and Fine-Tuning. Each has strengths, and the right choice depends on your specific requirements.

Understanding the Approaches

Retrieval Augmented Generation (RAG)

RAG keeps the base model unchanged but gives it access to your data at inference time.

How it works:

1. User submits a query

2. System retrieves relevant documents from your knowledge base

3. Retrieved documents are added to the prompt context

4. LLM generates response using this augmented context

Example:

User: "What's our refund policy?"

[System retrieves policy document from vector database]

Prompt to LLM: "Using the following policy document, answer the question...
[Policy document content]
Question: What's our refund policy?"

LLM: "According to your policy, refunds are available within 30 days..."

Fine-Tuning

Fine-tuning modifies the model itself using your data.

How it works:

1. Prepare training data (examples of inputs and desired outputs)

2. Train model on your data (adjusting model weights)

3. Deploy customised model

4. Model "knows" your domain without needing document retrieval

Example:

Training data:
{"input": "What's our refund policy?", "output": "Refunds are available within 30 days of purchase..."}
{"input": "How do I return an item?", "output": "To initiate a return, log into your account..."}
[hundreds or thousands more examples]

After training, model directly outputs domain-appropriate responses.

When to Use RAG

✅ RAG is ideal when:

1. Information changes frequently

If your knowledge base updates regularly (products, policies, prices), RAG pulls current information without model retraining.

2. Accuracy and traceability matter

RAG can cite sources. You can verify responses against retrieved documents.

3. You have limited training data

RAG works with whatever documents you have. Fine-tuning needs structured examples.

4. You need quick deployment

RAG can be implemented in days. Fine-tuning takes longer to prepare and train.

5. Multiple knowledge domains

Switch knowledge bases for different use cases without different models.

RAG Example Use Cases

• Customer support over product documentation

• Internal knowledge base Q&A

• Legal document analysis

• Medical information lookup

• Research assistant

When to Use Fine-Tuning

✅ Fine-tuning is ideal when:

1. You need specific styles or formats

Training examples teach the model your preferred tone, structure, and terminology.

2. Tasks are well-defined and consistent

Classification, extraction, and structured output generation benefit from fine-tuning.

3. Context window is limiting

Fine-tuned models "remember" without needing document context, saving tokens.

4. You have quality training data

Hundreds or thousands of input/output examples make fine-tuning powerful.

5. Response speed is critical

No retrieval step means lower latency.

Fine-Tuning Example Use Cases

• Brand-specific content generation

• Code generation for specific frameworks

• Domain-specific entity extraction

• Classification with consistent outputs

• Specialised writing styles

Comparison Matrix

The Hybrid Approach

Often, the best solution combines both:

Fine-tune for:

• Output format and style

• Domain terminology

• Task-specific behaviour

RAG for:

• Specific, current information

• Factual accuracy

• Source citation

Example Architecture:

User Query → Fine-Tuned Model (understands domain language and output format)
           → RAG retrieves specific data
           → Combined prompt generates accurate, well-formatted response

Implementation Considerations

RAG Requirements

Vector Database:

• Choose based on scale: Pinecone, Weaviate, pgvector, Qdrant

• Index your documents with appropriate chunking

Embedding Model:

• OpenAI ada-002, Cohere, or open-source alternatives

• Match quality to your use case

Retrieval Strategy:

• Semantic search baseline

• Consider hybrid (semantic + keyword)

• Reranking for improved relevance

Fine-Tuning Requirements

Data Preparation:

• Minimum ~100 examples (more is better)

• Consistent format and quality

• Cover edge cases

Training Infrastructure:

• OpenAI fine-tuning API (easiest)

• Cloud GPU for open-source models

• Experiment tracking

Evaluation:

• Hold-out test set

• Human evaluation for quality

• A/B testing in production

Cost Comparison

RAG Costs

• Vector database hosting (~$70-200/month for small-medium)

• Embedding generation (one-time for documents, per-query for queries)

• Increased prompt tokens (retrieved context)

Fine-Tuning Costs

• Training compute (one-time, but repeated for updates)

• Higher per-token inference cost for fine-tuned models

• Data preparation labour

Rule of thumb: RAG has lower upfront cost but higher per-query cost. Fine-tuning has higher upfront cost but can be cheaper at scale.

Our Recommendation

Start with RAG. It's faster to implement, easier to update, and provides explainability. Only move to fine-tuning when you have clear evidence that:

1. RAG isn't meeting your quality requirements, AND

2. You have sufficient training data, AND

3. The use case justifies the additional complexity

For many enterprise applications, well-implemented RAG is all you need.

---

Need help choosing and implementing the right approach? Let's discuss your use case.