What is Retrieval-Augmented Generation (RAG)?
A technique that enhances the accuracy and reliability of generative AI models with facts fetched from external sources.
RAG addresses the knowledge cutoff and hallucination issues of LLMs. Instead of relying solely on training data, a RAG system first retrieves relevant documents from a knowledge base (like a company's internal wiki) and provides them to the LLM as context for generating an answer.
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Prompt Engineering
The practice of designing inputs for AI models to produce optimal outputs.
Fine-tuning
The process of training a pre-trained model on a smaller, specific dataset to adapt it for a particular task.
Zero-shot Learning
The ability of a model to perform a task without having seen any specific examples of that task during training.