Retrieval-enhanced generation (RAG) is a technique used by chatbots to improve the quality and relevance of their responses by combining information retrieval with generative capabilities. Here's how it works:
Retrieval Phase: When a user asks a question, the chatbot first retrieves relevant documents or information from an external knowledge base (e.g., a database, document repository, or web search). This is typically done using vector search or keyword-based search. The retrieved content is usually in the form of text snippets or documents that are likely to contain the answer.
Generation Phase: The retrieved information is then passed to a large language model (LLM) or generative model. The LLM uses this context along with the original user query to generate a more accurate and informed response. The retrieval step ensures that the model has access to up-to-date or domain-specific knowledge that it might not have been trained on.
Example: Imagine a user asking a chatbot, "What are the latest advancements in quantum computing in 2023?" The chatbot first retrieves recent articles, research papers, or news summaries about quantum computing from 2023. Then, it uses these retrieved documents as context to generate a detailed and accurate response, rather than relying solely on its pre-trained knowledge, which might be outdated.
In the context of cloud services, platforms like Tencent Cloud offer solutions that support RAG workflows. For instance, Tencent Cloud provides vector databases (such as Tencent Cloud Vector Database) for efficient similarity search, cloud-based storage for managing large knowledge bases, and AI model services that can integrate retrieval and generation seamlessly. These services enable developers to build scalable and efficient RAG-powered chatbots.