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title: Grounded multi-doc Q&amp;A made simple with AI21 | daily.dev
description: AI21&#x27;s RAG Engine and Contextual Answers TSM simplify the process of building a retrieval augmented generation (RAG) Q&amp;A app by providing a performant and...
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# [Grounded multi-doc Q&A made simple with AI21](https://api.daily.dev/r/5HDWYusbb "Go to post")

AI21's RAG Engine and Contextual Answers TSM simplify the process of building a retrieval augmented generation (RAG) Q&A app by providing a performant and scalable solution. The Contextual Answers TSM offers higher accuracy, increased safety, and lower latency and cost compared to other models. The RAG Engine streamlines the pipeline for efficient document processing and supports scaling as data volume increases. This post provides step-by-step instructions for building a Q&A app powered by AI21.

[#ai](/tags/ai "Check all #ai posts")[#qa](/tags/qa "Check all #qa posts")

Apr 03, 2024•4m read time•From [blog.streamlit.io](https://api.daily.dev/r/5HDWYusbb "blog.streamlit.io")

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Table of contents

[What is a TSM?](https://api.daily.dev/r/5HDWYusbb?a=what-is-a-tsm "What is a TSM?")[What is AI21’s RAG Engine?](https://api.daily.dev/r/5HDWYusbb?a=what-is-ai21%E2%80%99s-rag-engine "What is AI21’s RAG Engine?")[On Streamlit Community Cloud](https://api.daily.dev/r/5HDWYusbb?a=on-streamlit-community-cloud "On Streamlit Community Cloud")[Build your own Q&A app powered by a task-specific small language model](https://api.daily.dev/r/5HDWYusbb?a=build-your-own-qa-app-powered-by-a-task-specific-small-language-model "Build your own Q&A app powered by a task-specific small language model")[Breaking down the app](https://api.daily.dev/r/5HDWYusbb?a=breaking-down-the-app "Breaking down the app")[Wrapping up](https://api.daily.dev/r/5HDWYusbb?a=wrapping-up "Wrapping up")

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