Fine-Tuning vs RAG: When to Use Each Approach

The Core Difference

Fine-tuning changes what the model knows — it learns your patterns, style, and domain knowledge permanently. RAG (Retrieval-Augmented Generation) changes what the model sees — it retrieves relevant context at query time without modifying the model.

When to Use Fine-Tuning

Fine-tune when you need consistent style, domain-specific terminology, or when your task pattern is well-defined but not well-served by general models.

import vincony

client = vincony.Client(api_key="YOUR_API_KEY")

# Fine-tune for your coding style
fine_tune = client.models.fine_tune(
    base_model="codestral",
    training_data="./code-samples/",
    task="code_generation",
    epochs=3,
    evaluation_split=0.1
)

print(f"Model ID: {fine_tune.model_id}")
print(f"Improvement: +{fine_tune.quality_gain}% on your codebase")

# Use your fine-tuned model
result = client.code.complete(
    model=fine_tune.model_id,
    prompt="Create a new API endpoint"
)

When to Use RAG

Use RAG when you need up-to-date information, when your knowledge base changes frequently, or when you need to cite specific sources.

# RAG pipeline
rag = client.rag.create(
    knowledge_base="./docs/",
    embedding_model="text-embedding-3-large",
    chunk_size=512,
    overlap=50
)

answer = rag.query(
    question="How do I configure authentication?",
    model="gpt-5",
    top_k=5,
    include_sources=True
)

print(answer.response)
for source in answer.sources:
    print(f"  [{source.relevance}%] {source.file}")

The Hybrid Approach

The best results often come from combining both: fine-tune for style and domain knowledge, then use RAG for specific facts and recent data.

Decision Framework

Use fine-tuning for: consistent output style, domain jargon, task-specific performance. Use RAG for: factual accuracy, changing knowledge, source citations, large knowledge bases.