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aishwaryanr/awesome-generative-ai-guide

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TLDR

Free, community-maintained learning hub with courses, research papers, and practical guides for understanding and building with generative AI.

Mindmap

mindmap
  root((repo))
    Learning Materials
      Monthly research digests
      90+ free courses
      Code notebooks
    Core Topics
      LLM applications
      Prompt engineering
      RAG systems
      AI agents
    Practical Resources
      Interview questions
      AI terminology glossary
      Production deployment
    For Builders
      Model selection
      Performance evaluation
      Hands-on experiments

Things people build with this

USE CASE 1

Learn how to build AI-powered applications by following structured courses on LLMs and prompt engineering.

USE CASE 2

Stay current with generative AI research by reading monthly digests of important papers summarized in plain language.

USE CASE 3

Run code notebooks to experiment with AI concepts like RAG and agents without setting up infrastructure.

USE CASE 4

Prepare for AI-related job interviews using the 60 common generative AI interview questions.

Tech stack

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Getting it running

Difficulty · easy Time to first run · 5min
Free to use and share for learning purposes; community-maintained and openly accessible.

In plain English

Awesome Generative AI Guide is a free, community-maintained learning hub for everything related to generative AI, the technology behind ChatGPT, image generators, and similar tools. It's not software to run; it's a curated library of learning materials, courses, research papers, and practical resources for anyone wanting to understand or build with AI. The collection is organized around several practical needs. There's a monthly digest of the most important new AI research papers, distilled into accessible summaries. There are free structured courses on topics like how to build applications with large language models (LLMs), prompt engineering (the craft of writing instructions that get better results from AI), RAG (Retrieval-Augmented Generation, a technique for giving AI models access to specific documents or databases), and AI agents (AI systems that can take sequences of actions autonomously). For founders and vibe coders building AI-powered products, this is a practical starting point: the course materials teach real-world skills like choosing the right AI model, evaluating whether your AI is performing well, deploying AI features in production, and understanding the limitations of current technology. There's also a dedicated section of 60 common generative AI interview questions, useful for hiring or for verifying your own understanding. The repository includes over 90 links to free GenAI courses from platforms across the web, a glossary of AI terminology explained in plain language, and code notebooks you can run immediately to experiment with AI concepts hands-on. It's actively maintained and updated as the field evolves rapidly.

Copy-paste prompts

Prompt 1
I want to build an AI chatbot for my product. Which course from this guide should I start with, and what are the key steps?
Prompt 2
Show me the prompt engineering section and explain how to write better instructions for AI models.
Prompt 3
I found a research paper in the monthly digest about RAG. Can you explain what Retrieval-Augmented Generation is and when to use it?
Prompt 4
Walk me through one of the code notebooks to show how to evaluate if my AI model is performing well.
Prompt 5
What are the top 10 generative AI interview questions from this guide, and how would you answer them?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.