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GenAI using Claude

GenAI using Claude by Netsetos: 12 course sections, 39 lessons, 195 quiz questions, 39 practice labs and 39 interview Q&A sets.

Build production AI applications on the Claude API — from first call to deployed system. This hands-on program covers prompting and tool use, structured outputs, retrieval-augmented generation (RAG), agents and the Model Context Protocol (MCP), evaluation, cost and latency optimization, and the shipping patterns that hold up under real traffic. What's inside: • 12 modules · 115+ hands-on steps • Runnable code and Colab notebooks in every lesson • Quizzes, practice labs, and interview Q&A throughout • Built for working software engineers by a Technical Architect with 15+ years of industry experience Pairs with the flagship GenAI Engineering course: learn the fundamentals there, go deep on the Claude stack here.

Course syllabus

  1. Module 1 - Console, SDKs & First Call
    1. 1.1 Console Setup
    2. 1.2 Python & TypeScript SDKs
    3. 1.3 Model Ladder & Pricing
    4. Q&A - 1.1
    5. Q&A - 1.2
    6. Q&A - 1.3
  2. Module 2
    1. 2.1 System Prompts & Persona Design
    2. 2.2 XML Scaffolding for Complex Inputs
    3. 2.3 Few-Shot & Structured Output
    4. 2.4 Vision & PDF Inputs
    5. Q&A - 2.1
    6. Q&A - 2.2
    7. Q&A - 2.3
    8. Q&A - 2.4
  3. Module 3
    1. 3.1 Prompt Caching Fundamentals
    2. 3.2 Extended Cache & Multi-Turn
    3. 3.3 Cache Observability
    4. Q&A - 3.1
    5. Q&A - 3.2
    6. Q&A - 3.3
  4. Module 4
    1. 4.1 Files API Lifecycle
    2. 4.2 1M Context Economics
    3. 4.3 Whole-Doc vs Chunked Reads
    4. 4.4 Citation-First RAG
    5. Q&A - 4.1
    6. Q&A - 4.2
    7. Q&A - 4.3
    8. Q&A - 4.4
  5. Module 5
    1. 5.1 Structured Citation Schema
    2. 5.2 Citation Page Refs
    3. Q&A - 5.1
    4. Q&A - 5.2
    5. Q&A - 5.3
    6. 5.3 Hover-Card Rendering
  6. Module 6
    1. 6.1 Tool Schemas + tool_choice
    2. 6.2 Parallel Tool Calls
    3. 6.3 Fine-Grained Tool Streaming
    4. 6.4 Error Recovery Loops
    5. Q&A - 6.1
    6. Q&A - 6.2
    7. Q&A - 6.3
    8. Q&A - 6.4
  7. Module 7
    1. 7.1 MCP Protocol Overview
    2. 7.2 Building an MCP Server (Python)
    3. 7.3 MCP Connectors & Registry
    4. 7.4 Sub-agents & Handoff Patterns
    5. Q&A - 7.1
    6. Q&A - 7.2
    7. Q&A - 7.3
    8. Q&A - 7.4
  8. Module 8
    1. 8.1 Claude Agent SDK
    2. 8.2 Skills
    3. 8.3 Hooks
    4. 8.4 Memory tool & context editing
    5. Q&A - 8.1
    6. Q&A - 8.2
    7. Q&A - 8.3
    8. Q&A - 8.4
  9. Module 9
    1. 9.1 Extended Thinking
    2. 9.2 Computer Use Fundamentals
    3. 9.3 Sandboxing & Permissions
    4. 9.4 Pro Mode end-to-end
    5. Q&A - 9.1
    6. Q&A - 9.2
    7. Q&A - 9.3
    8. Q&A - 9.4
  10. Module 10
    1. 10.1 Batch API Pipelines
    2. 10.2 Caching Economics
    3. 10.3 LLM-as-Judge Eval Harness
    4. Q&A - 10.1
    5. Q&A - 10.2
    6. Q&A - 10.3
  11. Module 11
    1. 11.1 Vercel Supabase Deployment
    2. Q&A - 11.1
    3. Q&A - 11.2
    4. Q&A - 11.3
    5. 11.2 GCP Cloud Run With Vertex Claude
    6. 11.3 Decision Matrix
  12. Practice Lab
    1. 1.1 Console Setup
    2. 1.2 Python & TypeScript SDKs
    3. 1.3 Model Ladder & Pricing
    4. 2.1 System Prompts & Persona Design
    5. 2.2 XML Scaffolding
    6. 2.3 Few-Shot & Structured Output
    7. 2.4 Vision & PDF Inputs
    8. 3.1 Prompt Caching Fundamentals
    9. 3.2 Extended Cache & Multi-Turn
    10. 3.3 Cache Observability
    11. 4.1 Files API Lifecycle
    12. 4.2 1M Context Economics
    13. 4.3 Whole-Doc vs Chunked Reads
    14. 4.4 Citation-First RAG
    15. 5.1 Structured Citation Schema
    16. 5.2 Citation Page Refs
    17. 5.3 Hover-Card Rendering
    18. 6.1 Tool Schemas + tool_choice
    19. 6.2 Parallel Tool Calls
    20. 6.3 Fine-Grained Tool Streaming
    21. 6.4 Error Recovery Loops
    22. 7.1 MCP Protocol Overview
    23. 7.2 Building an MCP Server (Python)
    24. 7.3 MCP Connectors & Registry
    25. 7.4 Sub-agents & Handoff Patterns
    26. 8.1 Claude Agent SDK
    27. 8.2 Skills
    28. 8.3 Hooks
    29. 8.4 Memory tool & context editing
    30. 9.1 Extended Thinking
    31. 9.2 Computer Use Fundamentals
    32. 9.3 Sandboxing & Permissions
    33. 9.4 Pro Mode end-to-end
    34. 10.1 Batch API Pipelines
    35. 10.2 Caching Economics
    36. 10.3 LLM-as-Judge Eval Harness
    37. 11.1 Vercel Supabase Deployment
    38. 11.2 GCP Cloud Run With Vertex Claude
    39. 11.3 Decision Matrix

Questions about this course

Is it really free?

Joining is free and needs no card. Any Claude API usage on your own account is billed by Anthropic, not Netsetos; lesson 1.3 covers model pricing.

How long does it take?

It is self-paced, so it depends on you. There are 39 lessons across 11 modules, each with a Q&A step and a practice lab.

Is there a certificate?

No. This course doesn't award a certificate.

What do I need to start?

Module 1 starts with setting up the Claude Console and installing the Python and TypeScript SDKs.

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