# Creating Your First QA Pipeline with Retrieval-Augmentation > Source: > Published: 2026-06-17 00:00:00+00:00 `InMemoryDocumentStore` `SentenceTransformersDocumentEmbedder` `SentenceTransformersTextEmbedder` `InMemoryEmbeddingRetriever` `ChatPromptBuilder` `ChatGenerator` `OpenAIChatGenerator` `MistralChatGenerator` `TransformersChatGenerator` This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ( [RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever) for fetching relevant documents, [ChatPromptBuilder](https://docs.haystack.deepset.ai/docs/chatpromptbuilder) for creating a template prompt, and a [ChatGenerator](https://docs.haystack.deepset.ai/docs/generators) for generating the final answer.