What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is an AI framework that enhances LLM capabilities by providing access to external databases and knowledge sources, which help improve the accuracy and relevance of responses.

Luis Lastras, a director of language technologies at IBM Research, describes RAG as follows:

“It’s the difference between an open-book and a closed-book exam. In a RAG system, you ask the model to respond to a question by browsing through the content in a book, as opposed to trying to remember facts from memory.”

Now, let’s dive into RAG’s workflow for a better understanding.

How does RAG work?

As the name suggests, RAG consists of three main components, which work together to generate accurate responses.

1. Retriever

The retriever component inputs the user query and searches the entire knowledge base (databases, documents, web indexes) for relevant information. The main purpose is to gather information directly related to the query to support an accurate response.

The following are some types of retrievers used in RAG:

• Sparse retrievers: Sparse retrievers, like TF-IDF or BM25, use keyword matching to find relevant information. This technique is generally faster as it focuses on exact keyword matches rather than understanding the deeper meaning or context of the text. They are effective for simple searches where specific keywords in the query are important to the answer.

• Dense retrievers: Dense retrievers, like BERT and GPT, use semantic similarity to fetch relevant information. This approach takes longer as it encodes the text into dense vector representations, capturing context and meaning rather than exact keywords. They effectively handle queries that rely more on contextual understanding rather than specific terms.

• Hybrid retrievers: Hybrid retrievers combine dense and sparse techniques to exploit both. They provide a balanced approach by improving the retrieval accuracy in less time.

2. Augmentation

The augmentation component combines the retrieved information with the original query to form an augmented query. This enriched query, including the user’s question and the retrieved information, is passed to the language model. This context gives the model a solid foundation to generate a factual and error-free response.

3. Generation

In the final step, the generation component uses the augmented query to generate a response. With the added context, the model generates accurate and up-to-date responses.

Why use RAG?

With the recent developments in generative AI, large language models (LLMs) have become intelligent enough to perform various natural language processing (NLP) tasks, from generating text and multilingual abilities to writing code and generating images. The sky is the limit for these models. But here comes the twist.

LLMs sometimes struggle with consistency and accuracy in their behavior. The two main reasons for this are hallucinations and knowledge cutoff.

• Hallucination: Hallucination refers to generating false and inaccurate responses when responding to queries out of training data. Think of it as an overconfident student eager to answer every question, even if they don’t fully understand it.

• Knowledge cutoff: The knowledge cutoff refers to the limitation that LLMs have training data only up to a certain period of time. It’s similar to an outdated map, which fails to show new cities or recent geographical changes. Similarly, LLMs do not have access to information beyond their training data cutoff.

RAG addresses these challenges by providing models with real-time access to external knowledge bases. Instead of relying solely on their pretrained knowledge, RAG enables the model to extract recent and relevant information from external data sources. By combining recent information with the model’s existing knowledge, RAG-based models generate factual and up-to-date responses to the query, making them a powerful solution for tasks where accuracy and current knowledge are essential.

Conclusion

In conclusion, RAG transformed the LLM capabilities through external knowledge integration. As industries continue integrating AI into operations, RAG’s ability to provide relevant, up-to-date, and contextually aware information will be important in enhancing AI applications across sectors like customer support, education, and healthcare. RAG is not just a tool but a bridge to a future where AI models are more informed, effective, and adaptable.

🚀 Want to learn more?

If you want to learn more about RAG, check out our following courses:

• Fundamentals of RAG with LangChain: This course covers the basic RAG concept and includes hands-on implementation of a real-world use case to help you build a strong foundation.

• Advanced RAG Techniques: Choosing the Right Approach: If you’re ready to go beyond the basics, this advanced course explores RAG techniques (like Naive RAG, Modular RAG, etc.), offering insights on selecting the best technique and building an RAG-powered chatbot.