**RAG (Retrieval-Augmented Generation)** is an architecture that enriches an LLM's responses by providing relevant external context at query time. The process takes place in three steps: 1. **Indexing**: Your documents are split into chunks and transformed into vectors (embeddings) 2. **Retrieval**: For each query, the most relevant chunks are retrieved via semantic search 3. **Generation**: The LLM generates a response based on the retrieved chunks ```python

**Fine-Tuning** consists of retraining a pre-existing model on your specific data to modify its base behavior. The process involves: 1. **Data preparation**: Creating question/answer pairs or example texts 2. **Training**: Adjusting the model's weights on your data 3. **Deployment**: Using the customized model ```python

RAG vs Fine-Tuning: When to Choose What? A Technical and Practical Guide

15 janvier 2026

12 min read

Ailog Team

Discover the key differences between RAG and Fine-Tuning, their optimal use cases, and how to choose the best approach for your AI project. Complete guide with code examples.

RAG vs Fine-Tuning: When to Choose What? Technical and Practical Guide

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Introduction

When facing an artificial intelligence project, one question comes up systematically: should you use RAG (Retrieval-Augmented Generation) or Fine-Tuning? These two approaches allow you to adapt an LLM (Large Language Model) to your specific needs, but they work in fundamentally different ways.

Choosing the wrong approach can be costly: months of wasted development, disappointing results, and a squandered budget. Conversely, the right choice can transform your AI project into a resounding success.

In this technical and practical guide, we will dissect these two methods, compare their advantages and disadvantages, and give you a clear methodology to make the right choice based on your context.

Learning Objectives

By the end of this article, you will be able to:

Understand the fundamental mechanisms of RAG and Fine-Tuning
Identify the key decision criteria for choosing between the two approaches
Evaluate the costs, timelines, and resources required for each option
Implement a hybrid strategy combining RAG and Fine-Tuning
Avoid design errors that cause projects to fail

Prerequisites

Basic knowledge of Python
Familiarity with LLM and embedding concepts
General understanding of OpenAI, Anthropic, or equivalent APIs
Basic knowledge of vector databases (a plus, but not required)

Understanding the Fundamentals

What is RAG?

RAG (Retrieval-Augmented Generation) is an architecture that enriches an LLM's responses by providing relevant external context at query time.

The process takes place in three steps:

Indexing: Your documents are split into chunks and transformed into vectors (embeddings)
Retrieval: For each query, the most relevant chunks are retrieved via semantic search
Generation: The LLM generates a response based on the retrieved chunks

DEVELOPERpython
# Simplified example of a RAG pipeline with Ailog
from ailog import RAGPipeline

# Pipeline configuration
pipeline = RAGPipeline(
    vector_store="pinecone",
    embedding_model="text-embedding-3-small",
    llm="gpt-4o"
)

# Document indexing
pipeline.index_documents("./docs/base_connaissances/")

# Query with automatically retrieved context
response = pipeline.query(
    "Quelle est la procédure de remboursement ?",
    top_k=5  # Number of chunks to retrieve
)

What is Fine-Tuning?

Fine-Tuning consists of retraining a pre-existing model on your specific data to modify its base behavior.

The process involves:

Data preparation: Creating question/answer pairs or example texts
Training: Adjusting the model's weights on your data
Deployment: Using the customized model

DEVELOPERpython
# Example of data preparation for Fine-Tuning (OpenAI format)
training_data = [
    {
        "messages": [
            {"role": "system", "content": "Tu es un assistant expert en droit français."},
            {"role": "user", "content": "Quel est le délai de rétractation pour un achat en ligne ?"},
            {"role": "assistant", "content": "Le délai de rétractation pour un achat en ligne est de 14 jours à compter de la réception du bien, conformément à l'article L221-18 du Code de la consommation."}
        ]
    },
    # ... hundreds/thousands of similar examples
]

# Launching fine-tuning via the OpenAI API
import openai

# Upload the training file
file = openai.files.create(
    file=open("training_data.jsonl", "rb"),
    purpose="fine-tune"
)

# Create the fine-tuning job
job = openai.fine_tuning.jobs.create(
    training_file=file.id,
    model="gpt-4o-mini-2024-07-18"
)

Detailed Comparison: RAG vs Fine-Tuning

Comparison Table

Criterion	RAG	Fine-Tuning
Data updates	Instant	Requires new training
Initial cost	Low to medium	High
Cost per query	Higher (retrieval + generation)	Lower (generation only)
Deployment time	Hours to days	Days to weeks
Source traceability	Excellent	Non-existent
Hallucination risk	Reduced (if well configured)	Present
Style customization	Limited	Excellent
Required data volume	A few documents are enough	Hundreds/thousands of examples
Technical expertise	Medium	High

Strengths of RAG

1. Always up-to-date data

RAG shines when your data changes frequently. Add a new document to your knowledge base, and it's immediately available for queries.

DEVELOPERpython
# Instant update with RAG
pipeline.add_document("nouvelle_politique_rh_2024.pdf")
# The document is now accessible for all queries

2. Traceability and transparency

Each response can be accompanied by the sources used, allowing for easy verification.

DEVELOPERpython
response = pipeline.query(
    "Quelles sont les conditions de garantie ?",
    return_sources=True
)

print(f"Réponse : {response.answer}")
print(f"Sources utilisées :")
for source in response.sources:
    print(f"  - {source.document_name}, page {source.page}")

3. No risk of "catastrophic forgetting"

The base model remains intact. You don't risk losing general capabilities by over-specializing your system.

Strengths of Fine-Tuning

1. Deep style customization

If you need the model to adopt a very specific tone, vocabulary, or response format, Fine-Tuning excels.

DEVELOPERpython
# After fine-tuning on your customer support data
# The model naturally adopts your brand tone

response = client.chat.completions.create(
    model="ft:gpt-4o-mini:votre-entreprise:support-v1",
    messages=[
        {"role": "user", "content": "J'ai un problème avec ma commande"}
    ]
)
# Response automatically formatted according to your standards

2. Reduced latency

Without a retrieval step, responses are generally faster.

3. Learning complex patterns

Fine-Tuning allows you to teach complex reasoning or response formats that RAG cannot easily capture.

Decision Tree: How to Choose?

Question 1: Does your data change frequently?

Yes → RAG strongly recommended

If your documents are updated daily, weekly, or even monthly, RAG is the obvious option. Fine-Tuning would require retraining for each significant modification.

Examples of "dynamic data" cases:

Product documentation
Blog articles / news
Evolving internal procedures
Product catalogs

Question 2: Do you need to cite your sources?

Yes → RAG mandatory

For regulated domains (legal, medical, finance) or simply to build trust, the ability to cite sources is crucial.

DEVELOPERpython
# RAG with citations
response = pipeline.query(
    "Quels sont les risques de ce médicament ?",
    citation_style="academic"
)

# Output:
# "Les effets secondaires courants incluent... [Source: Notice ANSM, 2024]"

Question 3: Are you looking to modify the model's fundamental behavior?

Yes → Fine-Tuning recommended

If you want the model to:

Always respond in a specific JSON format
Adopt a unique brand personality
Systematically apply a reasoning methodology

DEVELOPERpython
# Example: fine-tuned model to always respond in structured format
# Trained on hundreds of examples of this format

response = model.generate("Analyse ce contrat de vente")

# Automatically structured output:
# {
#   "parties": ["Vendeur SA", "Acheteur SARL"],
#   "objet": "Vente de matériel informatique",
#   "risques_identifies": [...],
#   "recommandations": [...]
# }

Question 4: What is your budget and timeline?

Situation	Recommendation
Limited budget, quick need	RAG
Comfortable budget, time available	Fine-Tuning possible
MVP / Proof of Concept	RAG
Mature product to optimize	Fine-Tuning or Hybrid

Question 5: What is the size of your training dataset?

Less than 100 quality examples → RAG

Fine-Tuning requires a significant volume of quality data. With few examples, you risk overfitting.

500+ well-structured examples → Fine-Tuning feasible

The Hybrid Approach: The Best of Both Worlds

In many cases, the best solution combines RAG and Fine-Tuning.

Typical Hybrid Architecture

┌─────────────────────────────────────────────────────────┐
│                    User Query                            │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│              RAG Module (Retrieval)                      │
│  - Search in the knowledge base                         │
│  - Retrieval of relevant chunks                         │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│           Fine-Tuned Model (Generation)                  │
│  - Understands the company's style and tone             │
│  - Generates a response based on RAG context            │
└─────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────┐
│                   Final Response                         │
│  - Factual content from RAG                             │
│  - Style and format from Fine-Tuning                    │
└─────────────────────────────────────────────────────────┘

Implementing a Hybrid Architecture

DEVELOPERpython
from ailog import RAGPipeline
import openai

class HybridAssistant:
    def __init__(self):
        # RAG pipeline for retrieval
        self.rag = RAGPipeline(
            vector_store="pinecone",
            embedding_model="text-embedding-3-small"
        )
        
        # Fine-tuned model for generation
        self.fine_tuned_model = "ft:gpt-4o-mini:votre-entreprise:assistant-v2"
    
    def query(self, user_question: str) -> dict:
        # Step 1: Retrieval via RAG
        relevant_chunks = self.rag.retrieve(
            query=user_question,
            top_k=5
        )
        
        # Step 2: Build prompt with context
        context = "\n\n".join([chunk.text for chunk in relevant_chunks])
        
        # Step 3: Generation with fine-tuned model
        response = openai.chat.completions.create(
            model=self.fine_tuned_model,
            messages=[
                {
                    "role": "system",
                    "content": f"""Tu es l'assistant de notre entreprise.
                    Utilise le contexte suivant pour répondre :
                    
                    {context}
                    
                    Si l'information n'est pas dans le contexte, dis-le clairement."""
                },
                {"role": "user", "content": user_question}
            ]
        )
        
        return {
            "answer": response.choices[0].message.content,
            "sources": [chunk.metadata for chunk in relevant_chunks]
        }

# Usage
assistant = HybridAssistant()
result = assistant.query("Comment fonctionne votre politique de retour ?")

When to Opt for Hybrid?

The hybrid approach is particularly relevant when:

✅ You need up-to-date factual data (RAG) AND a specific response style (Fine-Tuning)
✅ Your query volume justifies the investment in both approaches
✅ You have enough data for Fine-Tuning AND a document base for RAG
✅ Response quality is critical for your business

Common Mistakes to Avoid

Mistake 1: Choosing Fine-Tuning for the wrong reasons

❌ "I want the model to know my data"

Fine-Tuning is not meant to "memorize" factual information. It modifies the model's behavior, not its knowledge base.

✅ Solution: Use RAG for factual knowledge.

Mistake 2: Neglecting RAG data quality

❌ "I uploaded all my PDFs, it should work"

A poorly configured RAG with poorly structured documents will produce mediocre responses.

✅ Solution: Invest in data preparation:

DEVELOPERpython
# Chunking best practices
pipeline.index_documents(
    "./docs/",
    chunk_size=500,        # Size adapted to content
    chunk_overlap=50,      # Overlap for context
    metadata_extraction=True,  # Extract metadata
    clean_text=True        # Clean PDF artifacts
)

Mistake 3: Underestimating Fine-Tuning costs

❌ "Fine-Tuning is cheaper to use"

True for cost per token, but the total cost includes:

Data preparation (significant human time)
Training cost
Testing and iterations
Maintenance and retraining

✅ Solution: Calculate the TCO (Total Cost of Ownership) over 12 months before deciding.

Mistake 4: Ignoring latency in RAG architecture

❌ "My RAG works, but responses are slow"

Retrieval adds latency. For real-time applications, this is critical.

✅ Solution: Optimize your pipeline:

DEVELOPERpython
# RAG performance optimizations
pipeline = RAGPipeline(
    vector_store="pinecone",
    cache_enabled=True,           # Cache frequent embeddings
    async_retrieval=True,         # Asynchronous retrieval
    reranking=False,              # Disable if latency is critical
    top_k=3                       # Reduce number of chunks
)

Concrete Use Cases

Case 1: Technical Documentation Assistant → RAG

Context: 500-page documentation, monthly updates Choice: Pure RAG Reason: Dynamic data, need for citations

Case 2: Hotel Reservation Agent → Fine-Tuning

Context: Standardized reservation process, specific brand tone Choice: Fine-Tuning Reason: Very specific conversational behavior, little factual data

Case 3: E-commerce Customer Support → Hybrid

Context: Evolving FAQ + brand tone + order history Choice: RAG (FAQ) + Fine-Tuning (style) + API (customer data) Reason: Combination of different needs

Conclusion

The choice between RAG and Fine-Tuning is not a question of technical superiority, but of fit with your specific use case.

Remember these key principles:

RAG: For factual knowledge, dynamic data, and traceability
Fine-Tuning: For style, format, and specific behaviors
Hybrid: When you need both, and have the resources to do it well

Always start with RAG if you're hesitant. It's faster to set up, less expensive, and reversible. You can always add Fine-Tuning later if needed.

With platforms like Ailog, implementing RAG becomes accessible even to teams without deep ML expertise. Fine-Tuning remains an advanced optimization to consider once your RAG system is mature and your needs are clearly identified.

Additional Resources

Ailog Documentation: Advanced RAG configuration
OpenAI Fine-Tuning Guide: Best practices
RAG vs Fine-Tuning comparative benchmark (our internal study)

Have questions about choosing between RAG and Fine-Tuning for your project? Contact our team for a personalized audit.