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Software Engineering in AI Era

Master modern software development with AI-powered tools and techniques

Learn how to leverage AI tools, build intelligent applications, integrate LLMs, and adopt best practices for developing software in the AI era.

Core Concepts You'll Master

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AI-Powered Development

Use AI assistants and tools to accelerate development workflows

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LLM Integration

Build applications with large language models and prompt engineering

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Machine Learning Fundamentals

Understand ML concepts, model training, and inference pipelines

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MLOps & Deployment

Deploy, monitor, and maintain AI models in production

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Ethics & Best Practices

Responsible AI development, bias mitigation, and security considerations

Why Software Engineering in AI Era?

AI is transforming how software is built, tested, and deployed. Modern engineers need to understand how to leverage AI tools effectively, integrate intelligent features into applications, and navigate the unique challenges of AI-powered systems. This course equips you with the skills to thrive in the AI-driven future of software development.

Start Learning: AI Foundations
Begin your journey into AI-powered software development!

Course Index

  1. Introduction to AI in Software Engineering
    Overview of AI tools, capabilities, and impact on development
  2. Machine Learning Fundamentals
    Core ML concepts, algorithms, and practical applications
  3. LLMs & Prompt Engineering
    Working with large language models and effective prompting techniques
  4. AI-Powered Development Tools
    Code assistants, testing tools, and productivity enhancers
  5. Building AI Applications
    Architecture patterns, APIs, and integrating AI into products
  6. MLOps & Model Deployment
    Deploying, monitoring, and maintaining AI models in production
  7. Ethics & Best Practices
    Responsible AI development, bias mitigation, and security
  8. MCP, RAG & CAG
    Augmenting LLMs with external knowledge and tools
  9. Mastering Claude
    The complete guide to Claude's ecosystem: Cowork, Models, Excel, Artifacts, Projects, and Code
  10. LangChain & LangGraph
    LangChain pipelines, LangGraph stateful agents, and framework selection guidance
  11. Google ADK
    Google's Agent Development Kit for building agentic AI workflows natively with Gemini and Vertex AI
  12. Prompt Engineering in Production
    Evaluation datasets, model-as-judge scoring, and CI/CD pipelines for reliable production prompts
  13. Agent Design Patterns
    ReAct, reflection, planning, routing, memory, and multi-agent topologies, framework-free
  14. Multimodal AI
    Vision, document understanding, audio, and image generation across models
  15. Securing AI Applications
    Prompt injection, the OWASP LLM Top 10, guardrails, and least-privilege agent security
  16. Structured Outputs & Tool Use
    JSON schemas, strict tools, the tool-calling loop, and parallel calls
  17. Context Engineering
    The context window as a budget: prompt caching, compaction, and what earns a slot
  18. Cost & Performance Engineering
    Model routing, effort levels, batching, streaming, and measuring what you spend
  19. Evaluation & Observability
    Tracing agent runs, model-as-judge scoring, and regression gates in CI
  20. Capstone: AI Stock Analyst
    Build a LangGraph multi-agent stock analysis app with Streamlit and Gemini 2.5 Flash

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