RAG vs. Fine-Tuning: Which Approach is Right for Your Business?

The rise of Large Language Models (LLMs) has brought businesses new opportunities to leverage AI for automation, decision-making, and content generation. Two of the most effective strategies for optimising LLM performance are Retrieval-Augmented Generation (RAG) and Model Fine-Tuning. But which approach is best suited for your business needs? In this blog post, we’ll break down the differences between RAG and fine-tuning, explore their use cases, and compare them in terms of security and cost.

What Are RAG and Fine-Tuning?

Retrieval-Augmented Generation (RAG) - the “Researcher”

RAG is an AI technique that enhances LLM outputs by integrating external knowledge sources. Instead of relying solely on pre-trained models, RAG retrieves relevant documents or data in real time and incorporates them into the model’s response. One common example of RAG in action is the AI Overview feature in search engines. For instance, when searching for “what is RAG” on Google, the search engine generates a concise summary from various sources while also displaying reference links to related pages.

Using RAG is like hiring a researcher—it searches for relevant information, retrieves the most useful documents, and then summarises or provides insights based on the gathered references. This approach ensures responses remain up-to-date and contextually relevant without modifying the core model.

Fine-Tuning - the “Expert”

Fine-tuning involves training an LLM on domain-specific data to adapt it for specialized tasks. This process updates the model's internal parameters, enhancing its ability to perform in specific areas such as medical diagnosis, financial analysis, or customer service automation.

Fine-tuning is like training an expert to acquire specialized skills. A general-purpose LLM, much like a university student in their early years, has a broad foundation of knowledge. However, fine-tuning refines the model’s expertise, similar to how students choose a major and receive further training in a particular field. This results in a model that excels in its specialised domain, providing more accurate and reliable outputs tailored to specific business needs.

Use Case

RAG Use Cases:

RAG, which functions as the researcher, excels in tasks requiring extensive information retrieval and real-time updates.

Many industries need to stay updated on changing regulations, legal precedents, and compliance requirements. RAG dynamically retrieves and summarises the latest case laws, regulatory updates, and compliance guidelines. This is especially beneficial for law firms, compliance officers, and financial institutions that must make legally sound decisions based on the latest data. Below image shows an example of RAG used in Environmental Planning and Biodiversity Conservation (EPBC) Act.

In academia, researchers need to stay informed about recent studies, reports and news. RAG can retrieve relevant academic papers, government reports, and industry insights in real time, saving professionals hours of manual research.

When purchasing products, customers spend time searching and comparing options. RAG enhances product recommendations by retrieving up-to-date product specifications, reviews, and availability details. This helps businesses provide personalised shopping experiences, boosting customer satisfaction and conversion rates.

Fine-tuning Use Cases:

Fine-tuning is most effective for domain-specific tasks where general LLMs struggle to deliver high accuracy. By training the model on specialised datasets, businesses can refine its reasoning and analytical abilities.

For example, fine-tuned models trained on medical literature and patient data can assist doctors in diagnosing diseases, recommending treatments, and detecting anomalies in medical imaging. Unlike RAG, fine-tuned models don’t need to fetch external data, making them faster and more reliable for time-sensitive medical decisions.

Businesses in agriculture require AI models to analyse weather patterns, soil conditions, and crop health to make informed decisions. A fine-tuned model can provide precise recommendations for irrigation schedules, pest control strategies, and yield optimisation. In some cases, fine-tuned models can outperform human expertise, offering data-driven insights for better farm management and resource utilisation.

Security

You may wonder whether RAG and model fine-tuning are secure to use in business. Unfortunately, they each have security challenges that need to be addressed during development. OWASP, a non-profit organization that aims to improve web application security, has published a list of security challenges for LLM (OWASP LLM Top 10) including RAG and fine-tuning. In the following sections, we will discuss the security challenges for each approach based on OWASP's list.

Security for RAG:

For RAG, the primary security concern is the vector database, which stores embeddings (numerical representations of text). Compared to standard databases, vector databases are more vulnerable because embeddings can be reverse-engineered back into their original text, potentially leading to data leaks—this is known as an Embedding Inversion Attack. To mitigate this risk, businesses must implement strict access controls and encryption to safeguard the vector database from unauthorised access or exploitation.

Security for Fine-Tuning:

For fine-tuning, the security challenges primarily revolve around the model training process. The datasets used for training may contain hidden vulnerabilities that only activate under specific conditions, making them difficult to detect with standard testing methods. Additionally, attackers can introduce data poisoning attacks, embedding hidden triggers within the dataset that cause the fine-tuned model to behave maliciously under certain inputs. Organisations must carefully vet their training data, employ adversarial testing techniques, and implement rigorous validation processes to prevent such attacks.

While both approaches have their security challenges, RAG systems generally offer more granular control and easier remediation of security issues. However, they require more complex runtime security controls. Fine-tuning presents fewer runtime security concerns but carries more serious risks of embedded vulnerabilities that are harder to detect and fix.

Cost:

When evaluating the costs associated with Retrieval-Augmented Generation (RAG) and Fine-Tuning, it's essential to consider the distinct expenses each approach entails.

Cost Considerations for RAG:

Implementing RAG primarily involves expenses related to building, inferring, and maintaining a vector database. The complexity and size of your dataset can significantly influence these costs, as larger datasets require more storage and computational resources.

For instance, Pinecone, a managed vector database service, offers serverless indexes that automatically scale based on usage. Their pricing model includes charges for storage and operations performed:

• Storage: 2GB included, with additional storage billed at $0.33 per GB per month.
• Write Units: 2 million operations included per month, with additional write operations starting at $4 per million.
• Read Units: 1 million operations included per month, with additional read operations starting at $6 per million.

These costs can accumulate based on the volume of data and frequency of operations, making it crucial to assess your specific usage patterns.

Cost Considerations for Fine-Tuning:

Fine-tuning involves training a model on domain-specific data, with expenses influenced by factors such as the size of the base model, the volume of training data, and the computational resources required. Utilising machine learning services like Amazon SageMaker allows businesses to select instance types that align with their model's complexity. For smaller models that don't require GPT, you can efficiently train using CPU-based instances like ml.t3.medium, which provide a cost-effective solution for lightweight training tasks. To accelerate training performance, GPU-enabled instances such as ml.g4dn can be employed, offering significant speed improvements while maintaining reasonable costs. For large language models (LLMs), Amazon SageMaker provides Trn1 or Trn2 instances, powered by AWS Trainium chips, which are specifically designed for high-performance machine learning training applications. These instances deliver optimal performance for handling the computational demands of large-scale models.

It's important to note that the choice of instance type directly impacts the cost and duration of the fine-tuning process. Training cost could range from less than $1 to around $20 per hour, depending on the instance type and region. While smaller models can be effectively trained on CPU instances or entry-level GPUs, larger models benefit from specialized hardware like Trainium. Therefore, businesses should assess their specific needs and select appropriate instances to balance performance and cost-effectiveness.

Conclusion

Choosing between Retrieval-Augmented Generation (RAG) and Fine-Tuning depends on your business’s specific needs, security requirements, and cost considerations. RAG excels in real-time information retrieval, making it ideal for industries that require up-to-date knowledge, such as legal, research, and e-commerce. In contrast, fine-tuning is better suited for domain-specific tasks that demand high accuracy and efficiency, such as medical diagnosis and agricultural analysis.

Ultimately, many businesses may benefit from a hybrid approach, leveraging RAG for external knowledge retrieval while fine-tuning models for specialized tasks. By carefully assessing your use case, security constraints, and budget, you can make an informed decision that maximizes the value of AI for your business.