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DevelopmentIntermediate59 lessons12 modules
Build AI Apps with Claude API
Build and deploy 3 production AI apps with the Claude API — chatbots, document Q&A, and internal tools. Ship real products, not demo projects.
$49per month — this curriculum and the other 13
Courses are not sold one at a time. One subscription opens this curriculum and every other one, each with its own coach. See the full terms. Card required to start the trial; the first payment lands on day 8 and renews until you cancel.
What's Included
- The full curriculum — 12 modules, 59 lessons
- An AI coach that knows this curriculum and your business
- Progress tracking across the curriculum
No videos, no written lesson pages, no quizzes, no certificate. You work through the curriculum in conversation with the coach.
7-Day Money-Back Guarantee
Not satisfied? Get a full refund within 7 days. No questions asked.
What You'll Learn
Master the Claude Messages API with streaming, tools, and vision
Build 3 complete AI projects with production deployment
Implement tool use and function calling patterns
Use extended thinking for complex reasoning applications
Build RAG applications with vector databases
Connect to MCP servers for universal AI tool integration
Build multi-agent systems with the Claude Agent SDK
Deploy AI apps to production with monitoring and cost controls
Outcomes
- Build and deploy 3 production-ready AI applications
- Master Claude API tool use, vision, RAG, and MCP integrations
- Design multi-agent systems with the Claude Agent SDK
- Ship AI features that handle real user traffic at scale
Prerequisites
- -JavaScript/TypeScript fundamentals
- -Basic web development (HTML, REST APIs)
- -Command line basics
Projects You'll Build
- AI customer support chatbot with tool use
- Document Q&A application with RAG
- AI-powered internal business tool
Course Curriculum
Module 1: Claude API Fundamentals
- 1.1Anthropic's model lineup — Haiku, Sonnet, Opus
- 1.2API setup — keys, SDKs, and your first API call
- 1.3Messages API deep dive — roles, system prompts, multi-turn
- 1.4Streaming responses — real-time output with SSE
- 1.5Error handling, rate limits, and cost optimization
Module 2: Prompt Engineering for Developers
- 2.1System prompts — configuring Claude's behavior
- 2.2XML tags and structured prompting
- 2.3Few-shot examples in API calls
- 2.4Temperature, top_p, and max_tokens — tuning output quality
- 2.5Prompt versioning and A/B testing in production
Module 3: Tool Use & Function Calling
- 3.1What is tool use? Claude calling your functions
- 3.2Defining tool schemas (JSON Schema format)
- 3.3Handling tool results and multi-turn tool use
- 3.4Building a weather bot, calculator, and web search tool
- 3.5Error handling and fallbacks in tool use
Module 4: Extended Thinking & Complex Reasoning
- 4.1Extended thinking mode — how and when to use it
- 4.2Building apps that require deep analysis
- 4.3Working with the 1M context window
- 4.4Structured analysis frameworks in production
- 4.5Cost vs quality tradeoffs
Module 5: Project 1 — AI Customer Support Chatbot
- 5.1Architecture design — embedding a chatbot in a web app
- 5.2Building the frontend (React/Next.js chat interface)
- 5.3Backend API routes with streaming responses
- 5.4Knowledge base integration
- 5.5Conversation history and context management
- 5.6Deployment to Vercel (production-ready)
Module 6: Vision & Multimodal Applications
- 6.1Claude's vision capabilities — what it can and can't see
- 6.2Image analysis API — sending images with messages
- 6.3Building a receipt/invoice scanner
- 6.4Document OCR and data extraction
- 6.5Combining vision with tool use for complex workflows
Module 7: RAG (Retrieval-Augmented Generation)
- 7.1What is RAG and why does it matter?
- 7.2Document chunking strategies
- 7.3Embeddings and vector databases (Pinecone, Supabase pgvector)
- 7.4Retrieval pipeline — query, embed, search, retrieve, generate
- 7.5Reranking and relevance scoring
- 7.6Evaluating RAG quality
Module 8: Project 2 — Document Q&A Application
- 8.1Full architecture — upload, process, index, query, answer
- 8.2File upload handling (PDF, DOCX, TXT, MD)
- 8.3Processing pipeline with progress indicators
- 8.4Chat interface with source citations
- 8.5Multi-document conversations
- 8.6Deployment and performance optimization
Module 9: Model Context Protocol (MCP) Overview
- 9.1What is MCP? The universal standard for AI tool integration
- 9.2Building a basic MCP server and connecting it to Claude
- 9.3Deep Dive Available: See our MCP & Tool Integration course for the complete MCP masterclass
Module 10: Project 3 — AI-Powered Internal Tool
- 10.1Designing an internal tool with Claude
- 10.2Authentication and role-based access
- 10.3Integrating with internal systems via MCP and tools
- 10.4Audit logging and compliance
- 10.5User feedback and continuous improvement
Module 11: Claude Agent SDK & Multi-Agent Systems
- 11.1The Claude Agent SDK (Python and TypeScript)
- 11.2Building a simple agent: plan, act, observe loop
- 11.3Subagents — delegating tasks to specialized agents
- 11.4Skills — packaging reusable agent capabilities
- 11.5Memory and state management across sessions
Module 12: Capstone & Production Readiness
- 12.1Project Portfolio: Package and Present Your 3 Apps
- 12.2Production Readiness Checklist
- 12.3Where to Go Next: Agents, MCP, and RAG deep-dive courses
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