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Generative AI Guide

Source: https://github.com/aishwaryanr/awesome-generative-ai-guide at f0185981c19f
License: MIT
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:star: :bookmark: awesome-generative-ai-guide

Section titled “:star: :bookmark: awesome-generative-ai-guide”

Generative AI is moving fast, and this repository is a comprehensive hub for generative AI research, courses, interview materials, notebooks, and more. Everything is now organized around one question: what do you want to do?

aishwaryanr%2Fawesome-generative-ai-guide | Trendshift aishwaryanr%2Fawesome-generative-ai-guide | Trendshift


Pick the door that fits you. Each journey has its own 101 to 301 path.


Pick your row (who you are) and your column (how far you want to go), then click in. Levels mean something different in each journey: depth is relative to that journey’s goal.

Journey101 Start 🟢201 Practitioner 🟡301 Advanced 🔴
🧑‍💻 Use AIwhat LLMs are; prompting basics; chat tools wellpower-prompting; projects; connecting tools; agent harnesses at workpersonal automation; multi-step agent workflows for yourself
🏗️ Build AIhow LLMs work enough to build; the app stack; your first LLM appRAG, agents, evaluation, fine-tuning, guardrailsLLMOps, production scale, advanced agents and RAG, security, system design
🔬 Understand AItransformers; key papers; how to read a paperactive research areas: reasoning, agents, RAG, evaluationthe frontier: monthly papers, research tables, State of AI

Build is the flagship journey and the deepest one. The 90+ free courses and code notebooks that used to be one long list are now sorted into these cells by level and tagged by topic.


Already know your subject? Jump straight to it. Each topic page gathers everything on that subject and shows which journey and level each piece serves.

LLM Foundations · Prompting and Context · Retrieval and RAG · Fine-tuning · AI Agents · Evaluation and Observability · Multimodal · Production and LLMOps · Safety and Security

Or see every free course in one place: All free courses, by topic.


Section titled “:mortar_board: Featured LevelUp Labs courses”

Free, created by Aishwarya Naresh Reganti and the LevelUp Labs team, several with certification:

For every free course (LevelUp Labs originals and vetted external), organized by topic, see the full All Free Courses, by Topic list.

Want to learn live with us? The courses above are free. We also teach cohort-based courses on Maven, and more than 3,000 builders have learned with us so far: AI System Design (design real generative-AI systems end to end, the Problem-First way) and Advanced AI Evals (the evaluation and improvement loops behind reliable LLM and agent products). See all our live courses.


Discover our favorite AI tools spanning every layer of AI application development. See Our Favourite AI Tools.


A full Role-Based Interview Prep hub: pick your role and work its folder end to end (overview, interview rounds, a deep question bank with answers, resources, courses, and a prep plan).

  • 🏗️ AI Engineer: build LLM apps, RAG, agents, evaluation, and system design.
  • 📋 AI Product Manager: product sense, metrics, tradeoffs, and responsible AI.
  • 🚀 Forward-Deployed Engineer: build, integrate, and deploy at the customer under ambiguity.
  • 🧭 AI Strategist: strategy, ROI, build vs buy, governance, and change management.

Everyone starts with the shared 60 GenAI Interview Questions, then works their role. System-design interview drills are planned for a later build phase.


Newest first:

  • OpenClaw Mastery for Everyone is live with certification. (Course)
  • AI Evals for Everyone is live with certification. (Course)
  • State of AI 2025 Report published, with its own build pipeline. (Report)
  • Agentic AI Crash Course released in 10 parts. (Course)
  • Living research tables for evaluation, agentic search and retrieval, and RAG are being kept current. (Research updates)


If you want to add to the repository or find any issues, please feel free to raise a PR. Place your addition in the right journey, level, and topic using the journey and level grid and the browse by topic index as your guide.


To cite this guide, use the format below:

@article{areganti_generative_ai_guide,
author = {Reganti, Aishwarya Naresh},
journal = {https://github.com/aishwaryanr/awesome-generative-ai-guide},
month = {01},
title = {{Generative AI Guide}},
year = {2026}
}

MIT License

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