https://github.com/nitindahiya-dev/gen_ai_roadmap
This guide is designed to help you master Generative AI through a structured, multi-phase journey
https://github.com/nitindahiya-dev/gen_ai_roadmap
agent-based agent-based-modeling agentic-ai ai ai-agent ai-agents airoadmap genai genairoadmap llm roadmap
Last synced: 5 months ago
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This guide is designed to help you master Generative AI through a structured, multi-phase journey
- Host: GitHub
- URL: https://github.com/nitindahiya-dev/gen_ai_roadmap
- Owner: nitindahiya-dev
- Created: 2025-04-18T09:39:42.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-18T10:21:18.000Z (about 1 year ago)
- Last Synced: 2025-10-23T05:46:01.667Z (8 months ago)
- Topics: agent-based, agent-based-modeling, agentic-ai, ai, ai-agent, ai-agents, airoadmap, genai, genairoadmap, llm, roadmap
- Language: HTML
- Homepage: https://gen-ai-roadmap.vercel.app
- Size: 13.7 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# GenAI Roadmap
Welcome to the GenAI Roadmap! This guide is designed to help you master Generative AI through a structured, multi-phase journey. Each phase builds on the last, culminating in a series of final projects to showcase your skills.
## 🚀 Phases Overview
1. **Foundations (Weeks 1–2)**
- Python refresher
- Basic AI/ML concepts
2. **Meet the LLMs (Weeks 3–4)**
- Explore major LLMs
- Hands-on with free/open models
3. **Build with Frameworks (Weeks 5–6)**
- LangChain basics
- Hugging Face Transformers
- LangGraph intro
4. **Data & Storage (Weeks 7–8)**
- Vector databases
- Graph databases
5. **RAG & Chat over Docs (Weeks 9–10)**
- Retrieval-Augmented Generation (RAG)
- Context-aware chatbots
6. **Agentic Workflows & Memory (Weeks 11–12)**
- AI agents
- Agent memory
7. **Advanced Integrations (Weeks 13–14)**
- Multi-modal LLM apps
- Document→Graph + embeddings
8. **Security & Guardrails (Weeks 15–16)**
- Prompt filtering
- Bias control
9. **Orchestration & Tooling (Weeks 17–18)**
- LangGraph orchestration
- Human-in-the-Loop
- Tool binding & calling
10. **Polishing & Fine-Tuning (Weeks 19–20)**
- LLM as judge
- Fine-tune a model
- Deploy & monitor
## 🎯 Final Projects
- **AI-Powered Legal Document Assistant**
- **AI-Powered Chart Builder with Postgres**
- **AI-Powered Resume Roasting**
- **AI-Powered Candidate Search**
- **AI-Powered Website Bot (Chat with Website)**
## 📅 Timeline
- **20 weeks total**: roughly five months of focused, part-time learning
- **Weekly goals**: build a tiny project each week
- **Milestones**:
- End of Month 1: simple chatbot
- End of Month 2: document Q&A system
- End of Month 3: agentic workflow
- End of Month 4: secure, multi-modal app
- End of Month 5: deployed, fine-tuned GenAI solution
## 📚 Resources
- Online platforms like LeetCode for Python practice
- Google Colab for free GPU access
- Pre-trained models from Hugging Face
- Neo4j for graph databases
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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By following this roadmap, you'll steadily progress from Python basics to building sophisticated, secure, and scalable GenAI applications. Good luck—and have fun exploring!