You have a large language model (LLM) that was pretrained by using public data.

The correct answer is D. Retrieval Augmented Generation (RAG). A pretrained LLM does not automatically know frequently updated proprietary content unless that content is supplied to it during use or the model is retrained. RAG solves this by retrieving relevant, current information from internal documents, knowledge bases, or indexed repositories at runtime and adding that information to the model’s prompt context. This keeps responses grounded in the latest approved business content without changing the model weights. Fine-tuning adapts the model but is more expensive and does not automatically keep up with frequently changing knowledge. Content filtering is a safety control, not a freshness mechanism. Prompt engineering improves instructions, but by itself it does not retrieve current proprietary content.