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An AI framework that retrieves relevant data from external knowledge sources (like databases or documents) before generating responses, improving the accuracy and factuality of Large Language Model outputs.
A customer support chatbot using RAG to look up specific policy documents in a company database before answering user questions about refund policies.
RAG is essential for building AI applications that need to access private data, real-time information, or specialized knowledge not in the model's training data.
RAG is faster to implement, doesn't require retraining the model, and can access real-time or frequently changing data.
RAG requires a vector database to store embeddings, a retrieval mechanism, and an LLM to generate responses based on retrieved context.
A type of artificial intelligence model trained on vast amounts of text data that can understand, ge...
A specialized database designed to store, index, and search high-dimensional vectors (numerical repr...
A numerical representation of text, images, or other data as a vector in high-dimensional space. Sim...
An AI chatbot developed by OpenAI that uses GPT (Generative Pre-trained Transformer) models to engag...
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