This course focuses on building real-world applications using the Claude API, moving beyond basic prompts to structured, reliable, and scalable API-driven systems.

Designed for developers and technical learners, the course teaches how to interact with Claude programmatically, structure API requests, manage multi-turn conversations, and generate consistent, machine-readable outputs using JSON. You’ll learn how to design applications that maintain context, validate responses, and handle tasks reliably. Through guided lessons and hands-on demonstrations, you’ll set up Claude API requests, build chat-based and text-processing applications, and progressively add context-awareness, task-based logic, and structured outputs. The course also covers advanced techniques such as streaming responses, error handling, performance optimization, and cost control. You will explore how to maintain conversation state across multiple interactions, enforce output formats for downstream systems, design small task-based agents, and improve application reliability using retries, validation, and controlled workflows. By the end of this course, you will be able to: - Structure and send well-formed requests to the Claude API - Build multi-turn, context-aware API applications - Generate and enforce structured JSON responses - Design task-based mini agents for automated workflows - Implement streaming responses and real-time updates - Handle API errors, rate limits, and retries effectively - Optimize API usage for performance and cost This course is ideal for software developers, backend engineers, and AI practitioners who want to build production-ready applications using Claude’s API. A basic understanding of Python and familiarity with APIs will be helpful, but no prior experience with advanced AI systems is required. Join us to learn how to design reliable, efficient, and scalable applications powered by the Claude API.

This course focuses on building reliable, production-ready AI systems using Claude, Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG).

You will begin by learning the fundamentals of MCP, including why it exists, how MCP servers work, and how Claude interacts with tools, resources, and external integrations through a controlled server-based architecture. You will build MCP servers, expose tools and resources, and enforce strict input and output schemas to ensure predictable and safe system behavior. The course then moves into Retrieval-Augmented Generation, where you will design complete RAG pipelines. You will learn how to chunk documents effectively, generate embeddings, apply keyword and vector-based retrieval techniques, and improve results using ranking and reranking strategies. You will also integrate MCP servers directly into RAG workflows to create scalable and modular retrieval systems. In the final module, you will build agent-driven workflows using Claude. You will design planning and decision agents, coordinate multiple agents, and automate end-to-end workflows that combine RAG, tools, and structured decision-making. By the end, you will be able to build fully automated AI systems that retrieve information, reason over it, and take action reliably. By completing this course, you will be able to: - Explain MCP architecture, including clients, servers, tools, and resources - Build MCP servers that safely expose tools, files, databases, and APIs to Claude - Design and enforce structured input and output schemas for reliable AI behavior - Implement complete RAG pipelines using chunking, embeddings, ranking, and reranking - Integrate MCP servers as retrieval backends for modular RAG systems - Build planning agents and multi-agent workflows using Claude - Automate end-to-end AI workflows that combine retrieval, reasoning, and tool execution This course is ideal for developers and AI practitioners who want to move beyond simple prompt-based applications and build scalable, controllable, and production-ready AI systems using Claude. Basic familiarity with Python and APIs is recommended, but no prior experience with MCP or RAG is required. Join us to learn how to design modern AI architectures that combine MCP, RAG, and agent workflows into real-world, production-ready systems.