A Q&A system is an AI-driven application designed to answer user queries by extracting relevant information from a dataset or knowledge base. These systems can be open-domain, capable of answering general questions, or closed-domain, focusing on specific topics.
The system typically works in three stages: question analysis, information retrieval, and answer generation. First, it analyzes the question to understand its intent and key entities. For example, in “What is the capital of France?” the system identifies “capital” and “France” as critical components.
Next, the system retrieves relevant information from a database or text corpus. Retrieval-augmented systems, such as those combining large language models (LLMs) with vector databases, excel in this step by finding semantically similar passages.
Finally, the system generates or extracts a concise answer. Advanced Q&A systems use transformers like GPT or BERT to synthesize human-like responses based on the retrieved context.
Q&A systems are used in customer support, virtual assistants, and educational tools. They enhance user experience by providing quick, accurate answers while reducing manual effort.