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Rag Interview System

Rag Interview System is an open-source collection of retrieval-augmented generation interview questions and answers, architecture references, system-design scenarios, study materials and Python lab notebooks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rag Interview System — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when preparing for RAG or LLM engineering interviews, organizing a study plan, comparing architecture patterns, or practicing interview questions. The notebooks are a separate, hands-on part of the repository for readers who want to work through a RAG lab.

Who it is for

  • AI and machine-learning engineers preparing for RAG, LLM or GenAI interviews
  • Software engineers moving into LLM application development
  • Data scientists preparing for RAG system-design interviews
  • Hiring managers and interviewers assembling GenAI question sets

Documented uses

  • Review RAG concepts such as embeddings, chunking, retrieval, vector databases and reranking.
  • Compare RAG architecture patterns using the 52-type cheatsheet.
  • Follow a structured learning path for interview preparation.
  • Practice questions with browser-based flashcards, quizzes and self-assessments.
  • Study or open a Python notebook lab for a RAG pipeline.
Before you choose

Requirements & limits

The repository is public on GitHub and identified as MIT-licensed. Its README describes browser-based quiz and assessment tools, Colab notebook links, and a local Python lab setup.

  • The repository describes 1,509 interview questions and answers spanning 52 RAG architecture types.
  • Study formats include reference guides, a comparison cheatsheet, interactive flashcards, auto-graded questions and timed or untimed self-assessments.
  • The project combines reference content with Python lab notebooks; JavaScript is listed as the repository's primary language.
  • Local lab setup calls for Python, Jupyter, the listed dependencies and AI provider credentials.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the local RAG lab environment

Prepare the repository's Python lab directory and open the first notebook in Jupyter.

Before you start

  • Git
  • Python 3
  • An AI provider, model and API key to enter in the environment file
  • JupyterLab and packages listed in the lab requirements file

Steps

  1. Clone the repository and change into its 06_labs_py directory.

  2. Create and activate a Python virtual environment.

  3. Install the dependencies with pip using requirements.txt.

  4. Copy .env.example to .env and set AI_PROVIDER, AI_MODEL and AI_API_KEY.

  5. Open 01_naive_rag.ipynb with JupyterLab.

Expected outcome

The local lab environment is prepared and the first RAG notebook is opened.

Guide limitations

  • The README documents setup and notebook opening, but this guide does not establish that the lab was run.
  • The required AI provider credentials must be configured in the environment file.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
12 October 2026
Original website title
ather-techie/rag-interview-system · Official homepage title · retained in its source language
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