Understanding how Close.is works is useful for building a mental model for the things it is good—and not good—at. It’s also helpful for understanding how you can get good answers from your bots.
Close.is answers questions using a process called retrieval augmented generation, or RAG for short.
Retrieval augmented generation is the process of supplementing a user’s input to a large language model (LLM) like ChatGPT with additional information that you have retrieved from somewhere else. The LLM can then use that information to augment the response that it generates.
This diagram shows how it works in practice:
It starts with an incoming question. For example “How do I do ?” The first thing that happens is the retrieval step. This is the process that takes that question and searches for the most relevant content from a knowledge base that might answer it. You can think of this part like a search engine for your uploaded content.
After retrieval, the relevant information is sent, along with the question, to the LLM (ChatGPT or GPT4). The LLM reads the information and then uses it to construct and answer.
What’s important to note is that only the relevant information from your knowledge base gets included in the answer. The entire context is never fully available.