Choosing Between Fine-Tuning and RAG for Domain-Specific Knowledge

You need an AI system that understands your domain. Should you fine-tune a model or use RAG? The answer depends on your problem.

The Problem Both Solve

Standard LLMs lack domain-specific knowledge:

• Your industry’s terminology and concepts
• Your company’s processes and standards
• Specialized reasoning patterns
• Your data and documents

Both fine-tuning and RAG add this knowledge. But they work differently.

Fine-Tuning Explained

Fine-tuning updates the model’s weights based on your data.

How it works:

1. Start with a pre-trained model
2. Train it on your domain data (hundreds to thousands of examples)
3. Model learns patterns in your data
4. Weights are permanently updated

Result: A new model that “understands” your domain.

RAG Explained

RAG retrieves relevant information and provides it as context.

How it works:

1. Store your documents in a vector database
2. User asks a question
3. Retrieve relevant documents
4. Include them in the prompt
5. LLM answers using the context

Result: The base model answers questions about your data without retraining.

Fine-Tuning vs. RAG: Head-to-Head

When to Fine-Tune

Fine-tune when you need the model to learn patterns and reasoning.

Good use cases:

• Medical diagnosis: Model learns diagnostic patterns from cases
• Code generation: Model learns your codebase style and patterns
• Legal analysis: Model learns to apply your firm’s legal reasoning
• Scientific research: Model learns domain-specific analysis methods
• Customer support: Model learns your support tone and processes
• Content generation: Model learns your brand voice and style

Requirements:

• Hundreds to thousands of quality examples
• Clear input-output pairs
• Consistent patterns to learn
• Budget for training compute

When to Use RAG

Use RAG when you need factual information from documents.

Good use cases:

• Knowledge bases: Answer questions about documentation
• Product support: Reference product docs for customer questions
• Compliance: Answer questions about policies and regulations
• Research: Analyze and cite source documents
• Internal wikis: Query company knowledge bases
• Contract analysis: Extract and answer questions about contracts

Requirements:

• Accessible documents or data
• Information that changes (or might change)
• Need for citations and sources
• Reasonable latency tolerance

Hybrid Approach: Fine-Tuning + RAG

The best solution often combines both:

Fine-tuned model + RAG:

1. Fine-tune on domain patterns and reasoning
2. Use RAG to provide current factual information
3. Model applies learned reasoning to retrieved facts

Example: Medical AI system

• Fine-tune on diagnostic reasoning patterns
• Use RAG to retrieve latest treatment guidelines
• Model diagnoses using learned patterns + current guidelines

Decision Tree

Do you have hundreds of quality examples?
├─ No → Use RAG
└─ Yes → Does your problem require learning patterns?
    ├─ No → Use RAG
    └─ Yes → Does information change frequently?
        ├─ Yes → Fine-tune + RAG
        └─ No → Fine-tune

Cost Comparison

RAG:

• Setup: $500-$5,000
• Monthly: $100-$1,000
• Update: Free (add documents)

Fine-tuning:

• Setup: $5,000-$50,000+
• Monthly: $100-$500 (inference)
• Update: $5,000-$50,000+ (retrain)

Fine-tuning + RAG:

• Setup: $10,000-$100,000+
• Monthly: $500-$2,000
• Update: $5,000+ (retrain) + free (add docs)

Implementation Considerations

Fine-tuning:

• Requires ML expertise
• Needs quality training data
• Takes time to prepare and train
• Difficult to debug
• Hard to update

RAG:

• Easier to implement
• Works with existing documents
• Fast to deploy
• Easy to update
• Easy to debug and improve

Real-World Examples

Stripe (Payment Processing):

• Fine-tuned model on payment patterns
• RAG for API documentation
• Result: AI that understands payments + current docs

OpenAI (GPT Fine-tuning):

• Customers fine-tune on their data
• Recommended for style, tone, format
• Not recommended for factual knowledge

Anthropic (Claude):

• Suggests RAG for knowledge
• Fine-tuning for reasoning patterns

Getting Started

Start with RAG if:

• You have documents to query
• You want to launch quickly
• Information changes frequently
• You have limited budget

Start with fine-tuning if:

• You have training examples
• You need custom reasoning
• You can invest in setup
• Performance is critical

In Calliope

RAG:

• Chat Studio: Connect documents, ask questions
• AI Lab: Build custom RAG pipelines
• Langflow: Visual RAG workflows

Fine-tuning:

• AI Lab: Fine-tune models on your data
• Deep Agent: Use fine-tuned models in agents
• API: Deploy fine-tuned models

The Bottom Line

• RAG for factual knowledge from documents
• Fine-tuning for learned patterns and reasoning
• Both for the best results

Start with RAG. Add fine-tuning if you need custom reasoning.

Build domain-specific AI with Calliope →