RAG shows up constantly in AI product descriptions, and it solves a genuinely specific problem: getting a model to answer using your actual, current data instead of only what it learned during training.
Retrieval-Augmented Generation works in two steps. First, retrieve the specific, relevant pieces of information from your own documents or database. Second, feed those retrieved pieces to the model alongside the question, so it answers based on your actual data rather than guessing from training data alone.
Our explainer on context windows covers why simply pasting your entire knowledge base into a huge context window tends to underperform a well-designed retrieval system pulling only the relevant sections first. RAG is that retrieval layer.
Internal company chatbots answering questions from private documentation, customer support tools referencing real product documentation, any application needing answers that reflect current, proprietary information, these are the classic use cases.
RAG is the difference between a model guessing from general training and one referencing your specific, current information. See a broader technical overview for more depth.
