their core limitations: hallucination—the production of content that is false or not grounded in factual information. RAG works by first retrieving information relevant to the user’s query from an external database or a collection of documents, and then generating an answer based on the retrieved information. This approach helps improve accuracy, particularly when responding with up-to-date information or domain-specific knowledge. However, since errors may still occur during the generation stage, it remains important to verify the sources used in the retrieved context. A common example of RAG in practice is the AI-generated summary answers now incorporated into search engines such as Google. Parameters The parameters of an AI model refer to the internal numerical values that influence how the model operates. In neural networkbased models, parameters consist of the weights (the strength of connections between neurons) and biases, which are gradually adjusted during the training process to improve performance. Picture a massive control panel covered with countless dials— training the model is like turning each dial little by little to find the optimal configuration. Generative AI models use billions to trillions of parameters to learn complex relationships between words, enabling them to generate Generative AI Guide for Civil Society