What is RAG? | How Retrieval-Augmented Generation Works? | RAG Explained | Edureka

Retrieval-Augmented Generation (RAG) is a hybrid AI approach that combines retrieval systems with generative models to deliver accurate, contextually relevant responses. RAG addresses limitations of traditional large language models (LLMs) by grounding responses in factual data, improving accuracy, and adapting to specific domains, making it suitable for various applications like chatbots and knowledge management systems.

What is RAG?

• 00:01:30 RAG is a hybrid approach that combines retrieval systems with generative models to produce accurate and contextually relevant responses. It bridges the gap between factual accuracy and natural language generation by using LLMs and incorporating factual data from a knowledge base or database.

Why is RAG important?

• 00:02:01 RAG addresses limitations of traditional LLMs by mitigating hallucinations, grounding responses in factual data, and dynamically accessing up-to-date information. It enhances accuracy and relevance, allows for smaller and more efficient models, and adapts to domain-specific knowledge for specialized applications. Additionally, it provides explainability by showcasing retrieved documents, increasing transparency and trust.

How RAG works?

• 00:03:10 RAG operates in a three-step process: a user query triggers the retrieval stage, where a retriever searches a database or knowledge base to fetch relevant information. This retrieved data is then processed by a generative model, like GPT or T5, which creates a coherent and contextually grounded natural language response. This hybrid approach makes it ideal for applications like chatbots and knowledge systems.

RAG applications

• 00:04:04 RAG has diverse applications across domains. In knowledge management, it can summarize large datasets and documentation for corporate teams. Legal and compliance tasks benefit from its ability to answer queries based on regulations and case law. Healthcare professionals can leverage it for research paper and guideline summaries. Education and e-learning can utilize it for virtual tutoring and detailed explanations. Interactive virtual assistants like Alexa and Siri can generate accurate and informative responses based on user queries.

RAG vs. Traditional AI

• 00:05:01 RAG offers several advantages over traditional AI models. It provides highly accurate responses using real-time data, adapts quickly to new queries with live data, and is easily updated by changing data sources. Traditional models, on the other hand, provide less accurate answers, offer fixed answers based on pre-trained knowledge, and require retraining for updates. RAG is ideal for knowledge-based tasks, while traditional models are better for creative tasks.