What on earth is RAG?

Consider a user asking an AI chatbot, "What is the capital city of country X?" Without RAG, the AI model tries to answer based on the pre-existing knowledge it acquired during training. However, if it's not up-to-date or has incomplete data, the AI model might provide inaccurate or irrelevant information.

With RAG, when the user asks the same question, the AI model searches an external database or knowledge repository containing information about countries and capitals. It retrieves the correct, up-to-date answer from this external source and incorporates this information into the response. In this case, the AI model provides the accurate capital city of country X.

Here's another example: Say you're trying to ask a recommendation system, "What are the top 5 movies directed by Christopher Nolan?" Without RAG, the AI model might give an outdated list or not understand the context if it hasn't been trained on Christopher Nolan's filmography.

With a RAG-enhanced system, the AI model refers to an external database containing information about directors and their movies. It retrieves the necessary information about Christopher Nolan's movies, ranks them based on ratings or popularity, and shares the accurate top 5 movie recommendations with you.

What is RAG?

Retrieval Augmented Generation (RAG) is an advanced AI technique used to optimize the outputs of large language models (LLMs), such as GPT-4 by OpenAI. RAG addresses the problem of hallucinations, or machine-generated information that is factually incorrect or unrelated, which stem from the shortfalls in AI training.

With RAG, AI engineers can pull external data from various sources like databases, document files, or APIs, and incorporate them into the learning process. The data is translated into a numerical library the model can understand, allowing the system to reference the library of information when responding to a user's query. Consequently, the model produces contextually appropriate and accurate results.

RAG in Real-World Applications:

Customer Support:

Companies can use RAG-based AI chatbots to assist customers with more accurate information by pulling data from a knowledge base or database containing product specifications, policies, and other relevant information. This allows the chatbot to have almost real-time access to crucial data, resulting in quicker and more precise customer service.

Academic Research:

RAG-based AI systems can be invaluable for academic researchers as they can sort through vast amounts of research data, documents, and publications efficiently. The AI system can reference relevant information from the library, which allows researchers to receive contextually appropriate results and deeper insights into their research interests.

Legal Services:

A RAG-powered AI tool can be beneficial in the legal domain, where parsing and understanding large volumes of documents and information is vital. The AI tool can access databases of legal documents, court judgments, and legislations, providing more relevant and context-specific results to legal practitioners.

Healthcare:

RAG can optimize medical diagnosis and treatment recommendations through AI systems that access and reference updated medical libraries, research papers, and databases. These systems can assist doctors by providing relevant, up-to-date findings and treatment options based on patient history, symptoms, and data from prior cases.

Content Creation:

Writers, journalists, and content creators can benefit from RAG-enhanced AI assistants capable of accessing databases of facts, references, and sources, ensuring that the produced content is well-rounded, informative, and accurate.

Limitations and Future Developments:

RAG has certainly improved AI-generated outputs, but it is not without limitations. Complex context and intricately connected information might pose challenges for RAG-based AI models. Furthermore, as AI models develop, alternatives such as long context windows, like those found in Meta's Llama 3, are being considered to help improve the recall capability of the model.