🎯 Generative AI Developer Roadmap

A 6-month step-by-step roadmap to become a Generative AI Developer
Learn LLMs, RAG, LangChain, Agents, and Production AI while building 10+ projects.

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📌 Overview

This roadmap is divided into 9 phases plus bonus advanced topics, guiding you week by week.

By the end, you will be capable of building full-stack AI apps and be ready for GenAI Engineer / AI Product Developer roles.

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🗂️ Phase 1: Foundations (Week 1–2)

Goal: Build strong basics of GenAI & set up your environment.

Week 1

• 📖 Learn: Generative AI, LLMs
• 🌐 Overview: OpenAI & Hugging Face ecosystem
• ⚙️ Setup: Python, Jupyter, VS Code

Week 2

• 🛠 Hands-on: OpenAI ChatCompletion API
• 🎯 Project 1: CLI-based Chatbot
• ✍️ Intro: Prompt Engineering & Token Management

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🗂️ Phase 2: Prompt Engineering (Week 3–4)

Goal: Learn how to interact with LLMs effectively.

Week 3

• 🔍 Learn: Zero-shot, Few-shot, Role prompting
• Templates & structured prompts

Week 4

• 🎯 Project 2: Smart Email Generator
  • Input: Subject → Output: Professional Email Copy

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🗂️ Phase 3: LangChain Essentials (Week 5–6)

Goal: Learn LangChain basics & work with documents.

Week 5

• 📖 Learn: Document loaders, Chunking, Embeddings, Vector Stores

Week 6

• 🎯 Project 3: AI-Powered PDF Q&A Bot
  • Tools: LangChain, PyPDF, FAISS, OpenAI Embeddings

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🗂️ Phase 4: Retrieval-Augmented Generation (RAG) (Week 7–9)

Goal: Connect LLMs with your knowledge base.

Week 7

• 📖 Learn: Embeddings & Vector DBs (ChromaDB, Pinecone)
• Concepts: Chunking, Indexing, Cosine similarity

Week 8

• 🎯 Project 4: Resume Analyzer Bot

Week 9

• 🎯 Project 5: YouTube Video Q&A Bot

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🗂️ Phase 5: Agents & Tools (Week 10–12)

Goal: Build AI agents using external tools.

Week 10

• 📖 Learn: LangChain Agents (ReAct, MRKL)
• 🛠 Explore: Tool integration (Web search, APIs, Calculator)

Week 11

• 🎯 Project 6: Multi-Tool Research Assistant

Week 12

• 🎯 Project 7: AI Travel Planner

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🗂️ Phase 6: LangGraph & Multi-Agent Systems (Week 13–14)

Goal: Build systems with multiple collaborating agents.

Week 13

• 📖 Learn: LangGraph basics
• Concepts: Multi-Agent Orchestration

Week 14

• 🎯 Project 8: Autonomous Startup Ideation Bot

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🗂️ Phase 7: API Development & Web Integration (Week 15–17)

Goal: Serve LLM apps via APIs & integrate frontend.

Week 15

• 📖 Learn: FastAPI basics

Week 16

• 🎯 Project 9: AI Code Review API

Week 17

• 🌐 Frontend Integration (React/Next.js optional)
• 🚀 Deploy backend on Render / Vercel

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🗂️ Phase 8: Model Customization (Week 18)

Goal: Adapt models to users/domains.

• 🧠 Learn: Personalization techniques
• 📝 Prompt templates per user
• 🔧 Basics: Fine-tuning vs RAG

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🗂️ Phase 9: Deployment & Production AI (Week 19–21)

Goal: Make AI apps production-ready.

Week 19

• 📖 Learn: Caching, Rate limiting, Logging
• ⚡ Tools: Redis & Pinecone persistence

Week 20

• 📊 Monitoring: LangSmith, OpenTelemetry

Week 21

• 🎯 Project 10: Full-Stack AI Feedback App

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🏆 Bonus Phase: Advanced Topics (Week 22–24)

• Fine-tuning vs RAG (when to use each)
• Open-source LLMs: LLaMA, Mistral, Ollama
• Local Vector DBs & embedding models
• Cost optimization (token counting, streaming)
• Hugging Face Transformers usage

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📅 Schedule Summary

Month	Focus Area
1	Foundations + Prompt Engineering
2	LangChain Essentials + RAG basics
3	Agents & Multi-Agent Systems
4	API Development + Web Integration
5	Model Customization + Production AI
6	Advanced Topics + Portfolio polish

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✅ Final Outcome

By following this roadmap, you will:

• 🚀 Build 10+ real projects
• 📚 Master OpenAI, Hugging Face, LangChain, LangGraph, FastAPI, Vector DBs
• 💼 Be ready for GenAI Engineer / AI Product Developer roles

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