Azure AI Engineer — Complete Learning Package

Hands-On Labs (1–50)

• Create Azure OpenAI resource and deploy GPT model
• Call OpenAI API with prompts
• Implement prompt engineering (few-shot, chain-of-thought)
• Configure content filters
• Create Azure AI Search index and indexer
• Implement keyword, vector, semantic, and hybrid search
• Build RAG system with AI Search + OpenAI
• Add citations to responses
• Use Document Intelligence for data extraction
• Use Computer Vision and OCR
• Train Custom Vision models
• Implement Speech-to-Text and Text-to-Speech
• Use Language Service (NER, sentiment, key phrases)
• Build conversational AI with CLU and Bot Framework
• Deploy bots to Azure Bot Service and Teams
• Implement translation services
• Create AI enrichment pipelines with skillsets
• Build Copilot Studio bots
• Use OpenAI Assistants API
• Implement function calling
• Build streaming responses
• Implement prompt injection protection
• Build multi-turn conversational systems
• Implement face and speaker recognition
• Build document Q&A systems
• Build enterprise RAG pipelines
• Implement multi-modal AI solutions
• Build content moderation systems
• Create recommendation systems
• Deploy AI with private endpoints and cost control
• Implement Responsible AI practices
• Build full AI platform integrating all services

Major Projects

Core AI Systems

• Enterprise RAG solution
• Customer support chatbot
• Knowledge base search system
• Content moderation platform
• Document processing pipeline

Vision, Speech & NLP

• Image classification and defect detection
• Speech transcription and translation
• Sentiment analysis dashboards
• Named entity recognition systems
• Multi-modal AI applications

Advanced AI Applications

• AI-powered CRM systems
• Code generation assistant
• Legal/medical document analysis
• Real-time meeting transcription + summary
• AI writing and content generation platform

Industry Use Cases

• Healthcare document analysis
• Insurance claim processing
• Retail product categorization
• Financial report analysis
• Smart city analytics

Gotchas & Common Mistakes

• Token limits apply to both input and output
• Temperature 0 is not fully deterministic
• Content filters cannot be fully disabled
• AI Search free tier limitations (3 indexes, 50MB)
• Embedding dimensions must match model
• Chunk size heavily impacts RAG performance
• Prompt injection must be handled explicitly
• Rate limits vary per model (TPM)
• Vector models are not interchangeable
• System prompt consumes tokens
• Streaming does not reduce cost
• Function calling increases latency
• Index schema changes may require rebuild
• AI Search synonym maps improve recall, not precision
• Custom Vision requires diverse training images
• Speech APIs differ for real-time vs batch
• Bot state management is developer responsibility
• Embedding costs add up at scale
• Prompt shields are not foolproof
• Responsible AI testing is essential before production

RAG Implementation Playbook

• Define document corpus and use cases
• Choose chunking strategy (fixed, semantic, sentence-based)
• Select embedding model
• Create AI Search index with vector fields
• Chunk and embed documents with metadata
• Configure hybrid search (keyword + vector + semantic)
• Embed user query and retrieve top-k results
• Generate response using OpenAI
• Add citations and grounding
• Implement guardrails (content safety, prompt protection)
• Test with real queries
• Deploy with monitoring and cost controls