RAG vs Fine-Tuning Llama 2: When to Use Which?

Key Differences Between RAG and Fine-Tuning

Understanding the fundamental differences between RAG and fine-tuning is crucial for making the right architectural decisions.

What Is RAG in Llama 2?

Retrieval-Augmented Generation (RAG) is an approach where Llama 2 generates responses by using information retrieved from external sources at the moment a query is made. Rather than relying solely on the knowledge stored within the model's weights from its initial training, RAG enables the model to reference databases, documents, or other data sources dynamically.

How the Process Works

Using Python for Data Science, the RAG workflow follows a specific sequence to ground the AI's response in factual data:

"text-purple-400">from "text-blue-400">langchain.embeddings "text-purple-400">import OpenAIEmbeddings
"text-purple-400">from "text-blue-400">langchain.vectorstores "text-purple-400">import Chroma
"text-purple-400">from "text-blue-400">langchain.chains "text-purple-400">import RetrievalQA
"text-purple-400">from "text-blue-400">langchain.llms "text-purple-400">import LlamaCpp

# Load documents and create "text-blue-400">vector store
documents = load_documents("data/")
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)

# Create "text-blue-400">RAG "text-blue-400">chain
llm = LlamaCpp(model_path="./models/">llama-2-7b-chat.gguf")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    "text-blue-400">retriever=vectorstore.as_retriever(),
    chain_type="stuff"
)

# Query the "text-blue-400">RAG system
response = qa_chain.run("What is ">RAG?")
print(response)

What Is Fine-Tuning in Llama 2?

Fine-tuning is the process of training Llama 2 further on a custom dataset so that the model learns specific behaviors, styles, or task patterns. Unlike RAG, fine-tuning directly modifies the internal parameters of the model.

In fine-tuning, the model is exposed to many examples of input–output pairs. Over time, it learns to replicate these patterns more accurately. Once fine-tuned, the model produces responses based on its updated internal knowledge, without retrieving external documents at inference time.

"text-purple-400">from "text-blue-400">transformers "text-purple-400">import LlamaForCausalLM, LlamaTokenizer, "text-blue-400">TrainingArguments, "text-blue-400">Trainer
"text-purple-400">import "text-blue-400">torch

# Load pre-trained model and tokenizer
model = LlamaForCausalLM.from_pretrained("meta-">llama/">Llama-2-7b-hf")
tokenizer = LlamaTokenizer.from_pretrained("meta-">llama/">Llama-2-7b-hf")

# Prepare training data
train_dataset = prepare_finetuning_data("data/train.json")

# Training arguments
training_args = "text-blue-400">TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
)

# Create "text-blue-400">trainer
"text-blue-400">trainer = "text-blue-400">Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Start fine-tuning
"text-blue-400">trainer.train()

Step 1: Prepare and Store Knowledge

The RAG process begins by collecting documents such as PDFs, webpages, or internal notes. These documents are split into small chunks and converted into embeddings. The embeddings are stored in a vector database for fast similarity search.

Step 2: Convert the User Query into an Embedding

When a user asks a question, the query is converted into an embedding so it can be compared with stored document embeddings.

Step 3: Retrieve Relevant Context

The vector database searches for the most similar document chunks based on the query embedding.

Step 4: Build the RAG Prompt

The retrieved context is injected into the prompt along with clear instructions to answer only from the provided information.

Step 5: Generate the Final Answer

The language model uses the retrieved context to generate a grounded and accurate response.

Step 1: Dataset Preparation

Action: Collect "Prompt–Response" pairs. Goal: Create a high-quality dataset that reflects the exact behavior or tone you want Llama 2 to learn (e.g., medical summaries or professional tone).

Step 2: Data Formatting

Action: Convert the dataset into the specific format required by Llama 2. Goal: Ensure the model can read and process the training data correctly.

Step 3: Model Training (GPU)

Action: Run the training process on high-performance GPUs. Goal: Update the model's internal parameters (weights) so it aligns with your provided examples.

Step 4: Evaluation

Action: Compare the fine-tuned model against the original "base" model. Goal: Verify that the model performs better on your specific task before moving forward.

Step 5: Deployment & Inference

Action: Deploy the finalized model for real-world use. Goal: The model now responds in the new, specialized style it learned during training.

Real-World Use Cases of RAG

RAG is commonly used in scenarios where accuracy and data freshness are critical. Internal knowledge assistants often rely on RAG to answer employee questions based on policy documents, manuals, or internal reports. Since these documents change over time, RAG allows updates without retraining the model.

Another major use case is document-based question answering in regulated domains such as law, healthcare, and finance. In these settings, answers must be grounded in source material, and hallucinations can have serious consequences. RAG helps mitigate this risk by forcing the model to rely on retrieved evidence.

Real-World Use Cases of Fine-Tuning

Fine-tuning is better suited for problems where the task is well defined and stable. Classification tasks such as intent detection, sentiment analysis, or routing support tickets benefit from fine-tuning because the model learns consistent mappings between inputs and outputs.

Fine-tuning is also effective when a specific tone or response structure is required. For example, a chatbot designed to follow a brand's communication style can be fine-tuned to produce uniform responses without relying on external context.

Cost, Maintenance, and Scalability Comparison

From a long-term perspective, RAG systems are generally easier to maintain. Updating the underlying data does not require retraining the model, which reduces operational complexity. RAG also scales well as document collections grow.

Fine-tuned models, while faster at inference, require retraining whenever the desired behavior changes or when new examples are added. This increases both computational cost and maintenance effort. For learners and early-stage projects, RAG typically offers a more practical balance between performance and flexibility.

Can RAG and Fine-Tuning Be Used Together?

In mature systems, RAG and fine-tuning are often combined. A common approach is to fine-tune Llama 2 for consistent response structure or tone, while using RAG to supply factual or domain-specific information at runtime.

This hybrid strategy allows teams to benefit from both approaches without over-relying on either one.

# Hybrid approach combining fine-tuned model "text-purple-400">with "text-blue-400">RAG
"text-purple-400">from "text-blue-400">transformers "text-purple-400">import "text-blue-400">pipeline
"text-purple-400">from "text-blue-400">langchain.retrievers "text-purple-400">import VectorStoreRetriever

# Load fine-tuned model
fine_tuned_model = "text-blue-400">pipeline("text-">generation", model="./fine-tuned-llama2")

# Setup "text-blue-400">RAG "text-blue-400">retriever
"text-blue-400">retriever = VectorStoreRetriever(vectorstore=vectorstore)

# Combined function
"text-purple-400">def hybrid_rag_finetuning(query):
    # Retrieve relevant context
    context = "text-blue-400">retriever.get_relevant_documents(query)
    
    # Combine context "text-purple-400">with query
    prompt = f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
    
    # Generate "text-purple-400">with fine-tuned model
    response = fine_tuned_model(prompt, max_length=200)
    "text-purple-400">return response

Best Practices and Common Pitfalls

Choosing between RAG and fine-tuning should always be driven by the nature of the problem. RAG should be preferred when information changes frequently or when responses must be traceable to source documents. Fine-tuning should be used when behavioral consistency is the primary goal.

A common mistake is attempting to fine-tune a model to store large volumes of factual data. This approach is inefficient and often leads to outdated responses. Another pitfall is evaluating systems based only on response fluency rather than correctness and reliability.