Retrieval Augmented Generation (RAG): What is it all about?

Jan 10 / AI Degree

Recently, Retrieval Augmented Generation (RAG) is the talk of the town in the AI world. This concept is transforming how machines generate information by bridging the gap between external data sources and generative AI models.

Imagine a customer service chatbot handling a complaint about a delayed package. The chatbot not only has to understand the customer’s query but also retrieve the most up-to-date information from the company’s logistics system—like the tracking number, delivery history, and expected resolution timeline.

Traditional AI models might struggle to provide an accurate and specific response in this scenario because they lack access to real-time data. And this is the problem RAG solves.

By integrating retrieval with generation, RAG ensures AI models delivers precise, contextual, and actionable answers,  improving the customer experience. Let’s go deeper into RAG and uncover what it's really all about.

What is RAG?

At its core, RAG combines two essential components:

  1. Retriever: This part searches external data sources—such as documents, databases, or even web pages—to find relevant information based on a query.
  2. Generator: Using a language model, the generator processes the retrieved information to craft a response that is both accurate and contextually relevant.


Think of RAG as an intelligent system that doesn’t rely solely on its “memory” (i.e., its pre-trained knowledge) but actively retrieves and integrates fresh, external information to enhance its answers. This makes it particularly valuable in dynamic fields like news, customer support, or technical troubleshooting, where up-to-date accuracy is critical.

RAG not only boosts the factual correctness of AI-generated responses but also expands the applications of generative AI by allowing it to interact with real-world, constantly evolving data sources. This fusion of retrieval and generation makes it an indispensable tool for organizations looking to push the boundaries of automation and innovation.

How Does RAG Work?

Here’s how RAG functions step-by-step:

  1. A user submits a query.

  2. The retriever searches external sources, such as unstructured documents, SQL databases, or even live web content, to gather contextually relevant data.

  3. The generator synthesizes this information and creates a coherent response.

The magic lies in combining retrieval and generation in a seamless pipeline. This approach allows RAG to provide factually accurate, up-to-date, and well-contextualized responses, addressing some of the most significant challenges faced by traditional language models.

Unlike static models, RAG introduces a dynamic layer of adaptability that ensures answers stay relevant, even in rapidly changing domains.

Key Features of RAG

  1. Dynamic Knowledge Integration: RAG retrieves real-time data, enabling it to respond to queries about topics beyond its training cutoff, such as "Who won the Oscars this year?"

  2. Scalable Retrieval: Data sources can range from small datasets to massive, ever-changing repositories like the web.

  3. Context Chunking: External documents are broken into manageable pieces to fit within the AI model’s context limitations. This helps ensure optimal efficiency while maintaining relevance.

With these features, RAG stands out as a robust and reliable solution for industries where accuracy and timeliness are paramount. By adapting to new information on-the-fly, RAG ensures that users always get the most relevant and actionable responses.

Techniques in Retrieval

RAG employs two main retrieval techniques:

  • Term-Based Retrieval: Traditional keyword-based methods (e.g., BM25) fetch data containing relevant terms. These methods are fast and efficient, making them a practical choice for scenarios where speed is essential.

  • Embedding-Based Retrieval: Advanced methods use vector embeddings to locate data semantically similar to the query, providing deeper contextual understanding and accuracy.

These methods can also be combined in a hybrid search—a popular strategy to balance speed and precision. Hybrid systems leverage the best of both worlds, ensuring that the retrieval process is both cost-effective and accurate.

RAG Beyond Text: Tabular Data and Agents

RAG isn’t limited to unstructured documents. It can also work with structured data (like SQL tables) through processes like Text-to-SQL, where queries are translated into SQL commands to extract relevant data. This flexibility allows RAG to extend its capabilities to a wide range of use cases, including finance, supply chain management, and academic research.

Additionally, "agentic RAGs" go one step further by integrating tools like web search APIs. This enables AI models to actively fetch up-to-date online information or perform other external actions, making them even more versatile. Agentic RAGs can operate like autonomous assistants, dynamically pulling in information from multiple sources to respond to user needs in real-time.

Why RAG is a Big Deal

The importance of RAG can’t be overstated. Traditional AI models often rely on pre-trained knowledge, which quickly becomes outdated or insufficient. By introducing retrieval, RAG enables:

  • Real-Time Updates: Keeping up with fast-changing information in areas like news, healthcare, and technology.
  • Improved Accuracy: Reducing the chance of “hallucinated” (made-up) responses, which can occur when models generate answers without sufficient context.
  • Greater Versatility: Adapting to a wide range of domains and use cases, from customer service to advanced scientific research.

By leveraging external data sources, RAG overcomes the static nature of traditional language models, offering a level of dynamism and precision that is unmatched. This makes it a critical component of modern AI applications.

Applications of RAG

From answering customer queries to academic research, RAG shines in scenarios where reliable, up-to-date information is critical. Industries leveraging RAG include:

  • Healthcare: Assisting with medical research and patient queries, ensuring access to the latest treatments and studies.
  • Education: Providing real-time explanations and access to large learning repositories, empowering students and educators alike.
  • Customer Support: Enhancing chatbot performance by integrating FAQs, troubleshooting guides, and personalized solutions.
  • Legal and Compliance: Quickly retrieving relevant clauses, laws, or precedents to support legal professionals in their work.

The ability to provide accurate, timely, and well-contextualized responses makes RAG an invaluable tool across sectors, driving efficiency and innovation.

Challenges of Using RAG

While RAG is powerful, it’s not without challenges. The retriever’s quality heavily impacts the generator’s performance. Furthermore, managing external data sources—especially large or frequently updated ones—can be computationally expensive. Balancing accuracy, speed, and cost remains a critical area of research and development.

Looking ahead, RAG is expected to evolve further with advancements in retrieval algorithms and hybrid models. Innovations in areas like vector search, context optimization, and agentic workflows promise to make RAG even more efficient and scalable. As AI systems become more integrated into daily life, RAG could redefine how machines interact with human knowledge, ushering in a new era of intelligent automation.

Ready to Learn More?

If you’re intrigued by how technologies like RAG work and want to dive deeper into the world of artificial intelligence, there’s no better time to start learning. Whether you’re aiming to build AI systems, master data science, or explore groundbreaking concepts like RAG, AI Degree is the perfect way to get started.

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