Why RAG is Gaining Attention in 2025

In this blog post, I aim to explore the fundamentals of RAG (Retrieval Augmented Generation), step-by-step, and share insights into its advantages, mechanisms, and fine-tuning methods as I embark on a project to build my own RAG-powered AI Agent.

While 2023 was the year of Large Language Models (LLMs) like ChatGPT and LLaMA, many experts predict that 2024 will spotlight RAG and AI Agents. In fact, we are already seeing a growing number of companies leveraging RAG to develop AI Agents, making it an essential technology for the future.

Realizing that now is the perfect time to explore this field, I’ve decided to dive into RAG and start a project to create my own AI Agent. In this blog post, I will provide an overview of why RAG is gaining so much attention in 2024, how it works, and its basic principles.

What is RAG?

RAG is a framework designed to address some of the limitations of LLMs. By incorporating data from external databases during LLM execution, RAG provides users with more accurate and up-to-date information.

One key limitation of traditional LLMs is their inability to access the latest information or domain-specific data, as their knowledge is limited to the data used during training. This often leads to incomplete or irrelevant responses when handling questions about specialized or current topics.

RAG overcomes this limitation by leveraging external databases to retrieve the most relevant and up-to-date information. As a result, it enables LLMs to deliver precise answers tailored to the user’s queries, even for specialized or real-time topics.

Why is RAG Gaining So Much Attention?

The growing importance of Retrieval-Augmented Generation (RAG) stems from the increasing usability and widespread adoption of Large Language Models (LLMs). LLMs have now reached a level where they can be applied across various domains, delivering performance that enhances productivity for both businesses and individual users.

RAG has emerged as a breakthrough because it effectively addresses some of the key limitations of LLMs. While LLMs offer tremendous advantages, they are not without shortcomings. RAG provides a way to overcome these limitations, amplifying the strengths of LLMs while minimizing their weaknesses. This ability to combine the best of both worlds is what makes RAG stand out and why it has been receiving so much attention.

The Four Key Benefits of RAG

Retrieval-Augmented Generation (RAG) offers significant advantages in generating responses by leveraging customized internal data (Scalability) and providing user-specific answers (Flexibility). It also improves answer validation (Accuracy) and addresses security concerns that arise when using Large Language Models (LLMs).

1. Scalability

• LLMs are pretrained models, meaning they are limited to the knowledge available at the time of training. As a result, LLMs often fail to provide responses with real-time updates or company-specific data. RAG, on the other hand, dynamically searches and utilizes the latest or internal information, making it particularly effective when dealing with constantly changing data.
• For this reason, fine-tuning is often described as a “closed-book” approach, whereas RAG is likened to an open-book approach. In today’s fast-paced, information-rich environment, the benefits of RAG are even more pronounced.

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2. Flexibility

• RAG generates responses based on the latest information and internal data, allowing it to provide more user-specific and tailored answers.
• Traditional LLMs, which are trained solely on publicly available information, sometimes produce incorrect answers when asked about proprietary or up-to-date topics. By incorporating additional context into the query, RAG enhances accuracy and delivers responses tailored to the user’s needs.

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3. Accuracy

• One of the biggest limitations of LLMs is hallucination—where models produce plausible but incorrect information. To address this, OpenAI, for instance, has begun including URLs as references for certain answers. Similarly, RAG allows users to verify the provided responses by referring back to the internal or retrieved data sources.
• This verification capability ensures more reliable answers, which is particularly valuable for making critical decisions based on validated information.

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4. Security

• Training LLMs often raises significant security concerns. For example, OpenAI’s ChatGPT may inadvertently send user-provided data to OpenAI’s servers during the training process, making it unsuitable for use in certain enterprises. RAG minimizes this risk by sharing only limited portions of data as needed to generate a response.
• This enhanced security makes RAG a more attractive option for enterprises, which is why many organizations are actively developing and implementing RAG-based solutions.

Future Trends of RAG

The future of Retrieval-Augmented Generation (RAG) lies in the development of personalized AI agents. These systems will be capable of processing vast amounts of data in real time, providing users with fast and accurate answers to their queries.

To achieve this vision, however, several key challenges must be addressed. These include building efficient and precise retrieval systems, establishing robust databases, and seamlessly integrating these systems with RAG models. Overcoming these challenges will be critical to ensuring the success and scalability of RAG technology.