RAG vs Fine-Tuning MCQs (Top 10 with Detailed Explanations) – Generative AI Guide

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RAG vs Fine-Tuning: Top 10 MCQs with Detailed Explanations

As large language models (LLMs) become central to modern AI applications, two key techniques are widely used to adapt them for real-world tasks: Retrieval-Augmented Generation (RAG) and Fine-tuning. While both approaches enhance model performance, they serve different purposes and are suitable for different scenarios.

RAG improves factual accuracy by retrieving relevant external information at query time, making it ideal for dynamic and frequently updated knowledge. Fine-tuning, on the other hand, modifies the model’s internal parameters to align its behavior, tone, and task-specific capabilities.

Understanding the differences between these two techniques is essential for students, researchers, and AI practitioners working with generative AI systems. This MCQ set presents carefully designed questions that test conceptual understanding, practical use cases, scalability considerations, and real-world trade-offs between RAG and Fine-tuning.

Each question includes a detailed explanation to help you build strong conceptual clarity and prepare for exams, interviews, and advanced study in Generative AI.

RAG vs Fine-Tuning: A Simple Comparison

Retrieval-Augmented Generation (RAG) and Fine-tuning are two widely used techniques for improving the performance of large language models (LLMs). Although both methods enhance model usefulness, they work in fundamentally different ways and are suitable for different types of problems.

Aspect	RAG	Fine-Tuning
How it works	Retrieves relevant external documents at query time and uses them as context	Updates the model’s internal weights using additional training data
Knowledge updates	Easy – just update the document database	Difficult – requires retraining the model
Best for	Frequently changing or large knowledge bases	Consistent tone, style, or task-specific behavior
Infrastructure	Requires embeddings and a vector database	Requires training data and computational resources
Knowledge storage	External (documents, databases)	Internal (model parameters)
Use cases	Chatbots with company knowledge, website assistants, enterprise search	Structured outputs, domain-specific writing style, instruction alignment

In practice, many real-world systems combine both approaches. RAG provides up-to-date and factual information, while fine-tuning ensures consistent response quality, tone, and task alignment.

Practice Questions on RAG vs Fine-Tuning

1.

What is the primary purpose of Retrieval-Augmented Generation (RAG)?

A. To retrain the base model using domain-specific data
B. To retrieve relevant external information at query time and use it for response generation
C. To permanently increase the model’s context window size
D. To reduce the number of parameters in the model

Correct Answer: B

Explanation:

Retrieval-Augmented Generation (RAG) is designed to improve the factual accuracy and relevance of large language model outputs by providing external knowledge at inference time. Instead of modifying the model’s internal weights, RAG retrieves semantically relevant documents (using embeddings and vector search) based on the user’s query and includes this information in the prompt.

This approach is particularly useful when:

• The knowledge base is large or frequently updated
• The information is domain-specific or private
• Retraining the model is expensive or impractical

Unlike fine-tuning, RAG keeps the model unchanged and separates knowledge storage from model learning, making it scalable and flexible for real-world applications such as enterprise search, website assistants, and documentation chatbots.

2.

Which component of a large language model is modified during fine-tuning?

A. Context window size
B. Prompt template structure
C. Model weights and parameters
D. External vector database

Correct Answer: C

Explanation:

Fine-tuning involves updating the internal weights and parameters of a pretrained large language model using additional domain-specific or task-specific training data. This process adjusts how the model represents language patterns internally, allowing it to better perform a targeted task or adopt a specific behavioral style.

During fine-tuning:

• The model undergoes additional gradient updates using supervised training data
• Parameters are modified to reflect domain knowledge or output preferences
• The learned changes become permanently embedded in the model

It is important to note that fine-tuning does not modify the context window size, the external document store and the prompt template.

Unlike RAG, which retrieves knowledge dynamically at inference time, fine-tuning encodes knowledge directly into model weights. This makes it suitable for:

• Style control (formal, academic, conversational)
• Structured output formatting
• Task-specific behavior alignment

However, incorporating new factual knowledge through fine-tuning requires retraining, which can be computationally expensive and time-consuming.

3.

Which approach is most suitable for handling knowledge that changes frequently, such as company policies or product catalogs?

A. Fine-tuning
B. Retrieval-Augmented Generation (RAG)
C. Increasing model depth
D. Prompt shortening

Correct Answer: B

Explanation:

RAG is specifically designed to retrieve external knowledge dynamically at inference time. This makes it highly suitable for domains where information changes frequently, such as policy updates, pricing data, inventory details, or regulatory documents.

In contrast, fine-tuning embeds knowledge into the model’s weights. If the knowledge changes, the model must be retrained, which is computationally expensive and operationally inefficient.

RAG allows organizations to:

• Update documents in the knowledge base without retraining
• Maintain separation between knowledge storage and model reasoning
• Scale easily as data grows

Therefore, for dynamic and evolving knowledge environments, RAG is the preferred and scalable solution.

4.

A company wants its chatbot to consistently respond in a formal legal tone with structured output formatting. Which method is most appropriate?

A. Retrieval-Augmented Generation (RAG)
B. Fine-tuning
C. Vector database scaling
D. Increasing token limit

Correct Answer: B

Explanation:

Fine-tuning modifies the model’s internal parameters to align its behavior with specific stylistic, structural, or task requirements. If a chatbot must consistently generate responses in a formal legal tone with defined output formatting, fine-tuning provides long-term behavioral alignment.

RAG, on the other hand, focuses on retrieving factual information. While it can improve knowledge accuracy, it does not guarantee stylistic consistency across responses.

Fine-tuning is ideal when:

• Output style must remain consistent
• Responses follow a predefined template
• Task-specific reasoning behavior is required

Thus, for tone control and structural alignment, fine-tuning is the most appropriate method.

5.

What is a major limitation of fine-tuning when compared to RAG?

A. Requires vector similarity search
B. Cannot easily incorporate new knowledge without retraining
C. Produces shorter responses
D. Reduces semantic understanding

Correct Answer: B

Explanation:

Fine-tuning embeds knowledge directly into the model’s parameters. While this can improve task performance and stylistic alignment, updating knowledge requires retraining the model with new data.

Retraining is:

• Computationally expensive
• Time-consuming
• Operationally complex

In contrast, RAG allows immediate knowledge updates by simply modifying the external document store. No retraining is required. This makes RAG significantly more flexible in rapidly evolving domains.

6.

In a RAG pipeline, what is the primary function of embeddings?

A. Updating neural gradients
B. Compressing token sequences
C. Enabling semantic similarity search for document retrieval
D. Increasing model creativity

Correct Answer: C

Explanation:

Embeddings transform textual data into high-dimensional numerical vectors that capture semantic meaning. In a RAG system, both user queries and documents are converted into embeddings.

The system then performs similarity search to identify documents whose embeddings are closest to the query embedding. This enables contextually relevant retrieval beyond simple keyword matching.

Thus, embeddings are the core mechanism enabling semantic retrieval in RAG architectures.

7.

Why do many production systems combine RAG and fine-tuning?

A. To eliminate token limitations
B. To achieve both knowledge grounding and consistent model behavior
C. To remove the need for prompts
D. To reduce hardware usage

Correct Answer: B

Explanation:

RAG provides up-to-date factual knowledge by retrieving external documents, while fine-tuning aligns the model’s internal reasoning style and output structure.

Combining both allows systems to:

• Deliver accurate, grounded responses
• Maintain stylistic consistency
• Align with domain-specific requirements

This hybrid approach is increasingly common in enterprise AI deployments.

8.

Why is RAG considered more scalable for enterprise knowledge management?

A. It reduces model parameter size
B. It eliminates external storage
C. It separates knowledge storage from model parameters
D. It guarantees zero hallucinations

Correct Answer: C

Explanation:

RAG decouples knowledge from the model itself. Documents are stored externally in databases or vector stores, allowing independent updates without retraining the model.

This separation:

• Improves scalability
• Reduces maintenance cost
• Supports large and evolving knowledge bases

For enterprises managing thousands of documents, this architecture is significantly more efficient than embedding knowledge into model weights.

9.

Which statement best describes the cost trade-off between RAG and fine-tuning?

A. Fine-tuning is always cheaper
B. RAG has no operational cost
C. Fine-tuning has higher upfront training cost, while RAG has ongoing retrieval infrastructure cost
D. Both cost structures are identical

Correct Answer: C

Explanation:

Fine-tuning requires computational resources for training, dataset preparation, and validation. These costs occur upfront.

RAG avoids retraining but introduces ongoing infrastructure requirements such as:

• Embedding generation
• Vector database maintenance
• Retrieval computation during inference

Therefore, each method has different cost dynamics depending on system scale and usage patterns.

10.

How does RAG help reduce hallucinations compared to fine-tuning?

A. By increasing parameter count
B. By grounding responses in retrieved external evidence
C. By removing probabilistic decoding
D. By limiting output length

Correct Answer: B

Explanation:

Hallucination occurs when a model generates plausible but incorrect information. RAG mitigates this by supplying retrieved documents as grounding evidence.

Because the model generates responses conditioned on real retrieved content, factual reliability improves. However, RAG does not eliminate hallucinations completely — it reduces them by anchoring responses in external knowledge.

Fine-tuning improves behavior and task alignment but does not inherently guarantee grounding in external evidence.