Question Answering
An NLP task where the model provides direct answers to questions, either from a given context passage (extractive QA) or from general knowledge (open-domain QA).
Why It Matters
Question answering is the most intuitive way humans interact with information systems. It powers customer support bots, search engines, and enterprise knowledge systems.
Example
A medical QA system that receives 'What are the side effects of metformin?' and returns a specific answer extracted from medical literature with citations.
Think of it like...
Like asking a librarian a question and getting a direct answer with the relevant book open to the right page, rather than being handed a stack of books to search through.
Related Terms
Natural Language Processing
The branch of AI that deals with the interaction between computers and human language. NLP enables machines to read, understand, generate, and make sense of human language in a useful way.
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.