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 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!