What is RAG (Retrieval Augmented Generation)?

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Anubhav Sharma · Answer

Retrieval-Augmented Generation (RAG) is an AI framework that improves Large Language Model (LLM) accuracy by retrieving data from external, trusted knowledge bases before generating a response. Instead of relying solely on pre-trained memory, RAG connects LLMs to real-time or proprietary data, reducing hallucinations and providing citations for higher reliability.

Key Components of RAG:

Retrieval: The system searches external sources (documents, databases, web search) for information relevant to the user query.
Augmentation: The prompt to the LLM is enhanced (augmented) by adding this retrieved, relevant data to the original query.
Generation: The LLM uses its generative capabilities, supported by the new context, to create an informed, accurate answer.

Benefits of RAG:

Enhanced Accuracy: Reduces errors and hallucinations by grounding answers in factual evidence.
Real-time Data: Accesses up-to-date information without requiring constant model retraining.
Data Security: Allows businesses to connect private data (e.g., PDFs, emails) to AI models without exposing that data in the training set.
Source Citation: Enables users to verify information sources.

RAG Workflow:

Ingestion: Data is processed and stored in a vector database.
Querying: A user asks a question.
Embedding: The query is converted into a numeric format (embedding) to find similar data.
Retrieval & Generation: Relevant context is found, passed to the LLM, and the final answer is generated.

RAG is widely used for building intelligent, domain-specific chatbots, search tools, and specialized research assistants.

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What is RAG (Retrieval Augmented Generation)?

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