🎯 Generative AI Roadmap for Intelligent Application Development (With Azure Integration)

Objective

Leverage Generative AI and pre-trained ML models using APIs/SDKs (OpenAI, Azure, HuggingFace, etc.) to:

• Build intelligent business apps
• Solve real-world problems
• Integrate AI features into existing backends

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🛠️ Stage 1: Foundations of AI for Developers (Weeks 1–4)

✅ Goals:

• Understand AI's capabilities
• Learn Python and basic APIs

📚 Courses:

• AI For Everyone – Andrew Ng (Coursera)
• Python for Data Science and AI – IBM (Coursera)
• OpenAI GPT Fundamentals – FreeCodeCamp (YouTube)
• HuggingFace Course – Chapter 1–3

🔧 Tools:

• Python, VSCode
• Postman (API testing)
• Jupyter Notebook

☁️ Azure Add-ons:

• Microsoft Learn: Introduction to Azure AI Services
• Try deploying and calling a basic Azure AI service (like Text Analytics) from Postman or .NET

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🧠 Stage 2: Generative AI and Prompt Engineering (Weeks 5–8)

✅ Goals:

• Write effective prompts
• Use LLMs via APIs

📚 Courses:

• Prompt Engineering for Developers – DeepLearning.AI + OpenAI
• LangChain for LLM Application Development – YouTube Series
• Introduction to Generative AI – Google

🔨 Projects:

• Chatbot for PDF Q&A using OpenAI
• Meeting note summarizer

☁️ Azure Add-ons:

• Azure OpenAI Service Overview
• Test your prompts via Azure OpenAI Playground
• Create a Cognitive Services resource in Azure portal and get keys to use with HttpClient in .NET

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🔗 Stage 3: Integrating AI Into Applications (Weeks 9–14)

✅ Goals:

• Use APIs in real-world business tools
• Build internal tools with GPT/embeddings

📚 Courses:

• Building AI-Powered Apps with OpenAI and Azure – Microsoft Learn
• LangChain: Chat with Your Documents – Udemy
• AI Engineer Learning Path – DeepLearning.AI

🔨 Projects:

• Smart CRM assistant (summarization, notes)
• GPT-powered FAQ agent
• Azure-hosted domain-specific Q&A bot

☁️ Azure Add-ons:

• Use Azure AI SDK for .NET to call OpenAI endpoints directly from C# services
• Deploy your app in Azure App Service or Azure Functions for scalability
• Azure Cosmos DB (if your app needs scalable NoSQL data)

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🧩 Stage 4: Retrieval-Augmented Generation & Embeddings (Weeks 15–20)

✅ Goals:

• Build GPT apps with your own data
• Use embeddings and vector databases

📚 Courses:

• RAG Fundamentals – DeepLearning.AI
• Vector Databases 101 – Weaviate Academy
• LangChain Docs – Question Answering with Documents

🔨 Tools:

• Pinecone, Chroma, Weaviate
• LangChain, LlamaIndex
• OpenAI Embeddings API

☁️ Azure Add-ons:

• Use Azure AI Search + Azure OpenAI to build RAG solutions:
  • Use Azure AI Search for enterprise data indexing
  • Connect your indexed content to OpenAI for grounding and retrieval
• Use Azure Blob Storage to host private documents for your RAG app

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🚀 Stage 5: Deploy, Secure, and Optimize (Weeks 21–26)

✅ Goals:

• Move to production
• Handle tokens, latency, cost, and security

📚 Courses:

• Generative AI on Azure – Pluralsight
• Full-Stack GPT SaaS App – YouTube
• OpenAI Cookbook – GitHub

🔨 Tools:

• FastAPI / Flask
• Docker, Azure Functions/App Services
• Azure Monitoring, App Insights, CI/CD

☁️ Azure Add-ons:

• Use Azure Key Vault to manage API keys securely
• Deploy with Bicep or ARM templates for repeatable infra
• Monitor AI usage & cost via Azure Cost Management + App Insights
• Add role-based access using Azure AD authentication

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✅ Final Outcome (6-Month Goal):

• Use OpenAI & Azure OpenAI to build intelligent tools
• Integrate GPT into enterprise backends (.NET, Python, Node)
• Solve real-world problems with GenAI
• Architect, build, and deploy GenAI-powered apps using RAG, agents, and API chaining