RAG chatbot

RAG stands for retrieval-augmented generation. A RAG chatbot combines a large language model with a retrieval step: when a user asks a question, the system first searches a knowledge source (help articles, product docs, policies, past tickets) for the most relevant passages, then passes those passages to the model as context so the generated answer is based on real, current information rather than the model's general training data alone.

Mechanically, the knowledge source is typically broken into chunks, converted into vector embeddings, and stored in a vector database or search index. At query time the user's question is embedded and matched against that index to retrieve the closest passages. Those passages are inserted into the prompt, and the model generates a reply that draws on them, often with citations back to the source.

The main advantage of RAG over a plain language model is accuracy and freshness. Because answers are tied to retrieved content, a RAG chatbot can reflect information that didn't exist when the model was trained, stay aligned with your specific policies, and reduce hallucinations by giving the model authoritative material to work from. Updating the knowledge base updates the bot's answers. No model retraining required.

RAG isn't a silver bullet. Answer quality depends heavily on retrieval quality: if the index is missing content, poorly chunked, or returns irrelevant passages, the model can still produce wrong or vague answers. Good RAG systems invest in clean source content, sensible chunking, strong retrieval ranking, and grounding checks that keep the model from straying beyond what was retrieved.

Mercateer is built on this knowledge-first pattern. Each agent retrieves from your own docs, FAQs, and policies and grounds its answers in that material, so replies stay accurate and on-policy, closer to a well-built RAG system than a scripted decision tree that only knows the paths someone hand-wired in advance.