Guide · Open source
How to use awesome-generative-ai-guide: a reader's walkthrough
Updated · written with AI from the project's own documentation and checked against our catalogue data
awesome-generative-ai-guide is a free, MIT-licensed GitHub repository that collects links, courses, notebooks, paper lists and interview materials about generative AI. It is a reading and study guide, not software you install. This page shows you how to navigate it so you can pick a starting point and find the right material for your goal and level.
awesome-generative-ai-guideaishwaryanr/awesome-generative-ai-guide · ★ 30.1k · MIT Install options & downloads Quick start
- 1Pick a goal. Decide whether you want to use AI, build AI systems, understand research, prep for interviews, or watch videos.
- 2Find your level. Use the journey and level grid to choose the 101, 201 or 301 column that fits your current knowledge.
- 3Open the journey page. Click the linked journey file, such as journeys/build.md, and read the section for your level.
- 4Use topic pages for gaps. Jump to a topic page like Retrieval and RAG or AI Agents when you need one subject.
- 5Work through a course or role folder. Start a free course or an interview role folder and follow its own structure.
- 6Check listings before relying on them. Verify course details and access terms yourself, since the README does not vet each resource.
Start with the question: what do you want to do?
The README is organized around goals rather than a single course. Choose the route that matches your situation: use AI in your work, build AI systems, or understand the research. Each route has its own 101 to 301 path, where 101 is the start and 301 is the most advanced.
- Use AI in my work: journeys/use.md
- Build AI systems: journeys/build.md
- Understand the research: journeys/understand.md
- Preparing for an interview: the Interview Prep hub
- Browsing courses: courses.md
- Preferring video: youtube/README.md
Use the journey and level grid to pick your level
The README has a grid with journeys as rows and levels as columns. Find your row, then your column, and click through to the linked section. The levels mean different things in each journey, so depth is relative to that journey's goal.
For example, the Build journey's 101 level covers how LLMs work enough to build, the app stack and a first LLM app, while its 201 level covers RAG, agents, evaluation, fine-tuning and guardrails.
Browse by topic when you already know your subject
If you already know what you want to study, skip the journeys and use the topic index. It lists LLM Foundations, Prompting and Context, Retrieval and RAG, Fine-tuning, AI Agents, Evaluation and Observability, Multimodal, Production and LLMOps, and Safety and Security. Each topic page shows which journey and level each resource serves.
For a single list of courses and learning resources, use the course catalog at courses.md.
Try the free courses from the maintainers
The README features several free courses by Aishwarya Naresh Reganti and the LevelUp Labs team. Some are certified. The README lists AI Evals for Everyone (10 chapters on evaluating LLM and agentic systems), OpenClaw Mastery for Everyone, Agentic AI Crash Course (10 parts covering agents, tools, RAG, MCP, planning, memory and multi-agent systems), Generative AI Genius (a no-math beginner introduction) and Applied LLMs Mastery (2024 edition, archived).
The README notes that provider access terms vary, so check each listing before enrolling.
Prepare for a role-based interview
The Interview Prep hub has folders for AI Engineer, AI Product Manager, Forward-Deployed Engineer and AI Strategist. Each folder covers an overview, interview rounds, a question bank with answers, resources, courses and a prep plan. Everyone starts with the shared GenAI Interview Questions file before working their role.
The README also points to 10 system-design case studies in the AI System Design Case Studies collection.
Track monthly research papers
The Understand journey's frontier section links to a monthly paper archive, research tables and the State of AI report. The README lists a monthly best GenAI papers list under the research_updates folder. Check that folder for the most recent entries, since the list is updated over time.
Clone the repository (optional)
You can read everything online without installing anything. If you want a local copy, the README gives this command:
git clone https://github.com/aishwaryanr/awesome-generative-ai-guideWhat people use it for
- A beginner choosing a first generative AI path from the 101 column of the Use or Build journey.
- A developer looking up learning material on RAG, agents or evaluation through topic pages.
- A job seeker working through the AI Engineer or AI Product Manager interview folder.
- A reader tracking new generative AI papers through the monthly research lists.
Frequently asked questions
Do I need to install awesome-generative-ai-guide?
No. The README lists the method as read-online, and cloning the repository with git is optional. You can browse the linked pages directly on GitHub.
Is awesome-generative-ai-guide free?
The repository is licensed under MIT, and the featured LevelUp Labs courses are described in the README as free. Some cohort-based live courses on Maven are paid, and the README does not list their prices.
Does it have prerequisites?
The README does not list any prerequisites. The 101 paths are described as starting points, so beginners can begin there.
Is this a tool or library I can run?
No. The README describes it as a collection of links and learning materials, not a runnable tool or library.
How is it different from awesome-llm or llm-course?
The README names awesome-llm, awesome-generative-ai and llm-course as comparable projects but does not compare them in detail. This repo's distinct features are its goal-based journeys, level grid, role-based interview prep and monthly paper lists.
Are the listed courses verified or up to date?
The README does not verify each listed resource, so check course content and access terms yourself before relying on them.
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Our verdict
Use this repo if you want a structured map of generative AI learning material, interview prep for AI engineering or product roles, or a monthly paper feed to follow. Pick something else if you need a runnable tool or library, or if you want every linked resource independently vetted.
Install options & downloads