# Generator

**Example** See [Tutorial 7](/docs/latest/tutorial7md) for a guide on how to build your own generative QA system.

While extractive QA highlights the span of text that answers a query, generative QA can return a novel text answer that it has composed. The best current approaches, such as [Retriever-Augmented Generation](https://arxiv.org/abs/2005.11401), can draw upon both the knowledge it gained during language model pretraining (parametric memory) as well as passages provided to it with a retriever (non-parametric memory). With the advent of Transformer based retrieval methods such as [Dense Passage Retrieval](https://arxiv.org/abs/2004.04906), retriever and generator can be trained concurrently from the one loss signal. Pros * More appropriately phrased answers * Able to syntehsize information from different texts * Can draw on latent knowledge stored in language model Cons * Not easy to track what piece of information the generator is basing its response off of