RAG
Retrieval-Augmented Generation combines AI models with external knowledge retrieval for accurate responses.
In Simple Terms
It's like a student who is allowed to bring their textbook to an exam. Instead of relying only on memorized information, the AI can look up current facts in a library of documents before answering, making its answers more accurate and up-to-date.
Detailed Explanation
Retrieval-Augmented Generation (RAG) is a technique that enhances AI responses by combining language models with external knowledge bases. Instead of relying solely on training data, RAG systems first retrieve relevant information from documents or databases, then use that context to generate more accurate, up-to-date, and verifiable responses. This approach reduces hallucinations and enables AI to access company-specific or recent information not in its training data.
Related Terms
Context Engineering
Context engineering is the practice of structuring and managing context—including system prompts, RAG (Retrieval-Augmented Generation), memory, and few-shot examples—so AI systems have the right information to answer accurately.
Read moreNatural Language Processing
Technology that helps computers understand, interpret, and manipulate human language.
Read moreIntent Engineering
Intent engineering is the practice of aligning AI behavior with user goals and business outcomes—designing prompts and workflows so the AI understands and fulfills what users actually want.
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