Building with the Claude API

Course Description

“Building with the Claude API” is a comprehensive engineering-focused course designed to teach developers how to create reliable, scalable, and production-ready applications using Anthropic’s Claude models.

The course begins with API fundamentals and progressively advances into structured prompting, tool calling, evaluation pipelines, Retrieval-Augmented Generation (RAG), prompt caching, multimodal processing, MCP integrations, and autonomous agent workflows.

Students will learn not only how to interact with Claude models, but also how to architect robust AI systems capable of handling real-world enterprise workflows. Through extensive hands-on labs, learners will build intelligent assistants, structured data extraction systems, evaluation pipelines, RAG-powered knowledge bases, MCP servers, and autonomous engineering agents.

By the end of the course, participants will have practical experience designing modern AI applications that integrate external tools, databases, APIs, and enterprise knowledge systems while following best practices for reliability, observability, and AI safety.

Who is this course for

• Developers, backend engineers, and product teams building AI-powered applications with LLM APIs

• Engineers transitioning into Generative AI, Agentic AI, and modern AI orchestration workflows

• ML engineers and startup founders building RAG systems, enterprise copilots, and autonomous AI assistants

• Students and technical professionals seeking hands-on experience with MCP, multi-agent systems, and production-grade LLM engineering

Course Objectives

By the end of this course, learners will be able to:

• Understand the Claude model ecosystem, API architecture, and multi-turn conversational workflows

• Apply advanced prompt engineering, structured JSON generation, streaming, and evaluation techniques for reliable AI systems

• Build AI applications with tool use, function calling, multimodal processing, and prompt caching optimization

• Design and implement RAG pipelines, MCP integrations, multi-agent workflows, and intelligent routing systems

• Develop production-ready autonomous AI assistants with orchestration, monitoring, reliability, safety, and deployment best practices

Prerequisites

• Basic programming knowledge in Python or JavaScript/Node.js

• Familiarity with REST APIs and JSON

• Understanding of software engineering fundamentals, development environments, and command-line tools

• Beginner-level knowledge of AI/LLM concepts helpful but not mandatory

• Familiarity with vector databases, LangChain, backend development, or AI orchestration frameworks is beneficial

Course outline

Section 1: Foundations & Setup

1. Anthropic Overview: Understanding the Claude 3.5 & 3.0 Model Family

2. Getting Started: API Keys and Environment Setup

3. Anatomy of an API Request (Messages API)

4. Lab 1: "Hello, Claude" – Set up your development environment and successfully execute your first multi-turn conversation using a Python script or Node.js.

Section 2: Mastering the API Interface

1. System Prompts: Setting the Persona

2. Parameters: Controlling Output with Temperature and Top-p

3. Response Streaming for Real-Time UI

4. Handling Structured Data (JSON mode)

5. Lab 2: "The Structured Assistant" – Build a system that takes raw user input and outputs a validated JSON object for a specific use case (e.g., lead generation or ticket categorization).

6. Quiz: Accessing Claude with the API.

Section 3: Advanced Prompt Engineering

1. The Anthropic Prompting Philosophy: Being Clear and Direct

2. Structuring Prompts with XML Tags (The Claude Secret Sauce)

3. Few-Shot Prompting: Providing Examples for Consistency

4. Chain of Thought: Encouraging Step-by-Step Reasoning

5. Lab 3: "The XML Architect" – Refactor a messy "mega-prompt" into a structured template using XML tags to isolate instructions, context, and data.

6. Quiz: Prompt Engineering Techniques.

Section 4: Prompt Evaluation & Testing

1. Introduction to Evals: Why Intuition Isn't Enough

2. Generating Synthetic Test Datasets

3. Model-Based Grading (Using Claude to Grade Claude)

4. Code-Based Grading for Deterministic Outputs

5. Lab 4: "The Eval Pipeline" – Create a mini-eval suite that tests your Lab 2 assistant against a dataset of 20 edge cases and generates a performance report.

Section 5: Tool Use (Function Calling)

1. Introduction to Tool Use & Tool Schemas

2. Handling Message Blocks and Stop Sequences

3. Multi-Turn Conversations with Tool Results

4. Advanced: The Text Edit and Web Search Tools

5. Lab 5: "The Calculator & Search Agent" – Build an agent that can decide when to perform a math calculation versus when to "search" a mock database to answer a user query.

6. Quiz: Tool Use with Claude.

Section 6: RAG (Retrieval Augmented Generation)

1. The RAG Architecture: Retrieval vs. Context Window

2. Chunking Strategies & Embedding Generation

3. Hybrid Search: BM25 Lexical + Vector Search

4. Implementing the Full RAG Flow

5. Lab 6: "Knowledge Base Pro" – Implement a RAG pipeline using a PDF of technical documentation, allowing Claude to provide cited answers based on the text.

Section 7: Modern Features & Performance

1. Extended Thinking (Claude 3.7+ capabilities)

2. Vision Support: Processing Images and PDFs

3. Cost Optimization: The Rules of Prompt Caching

4. Code Execution & The Files API

5. Lab 7: "Optimizing the Pipeline" – Take your Lab 6 RAG system and implement Prompt Caching to reduce latency and cost for repeat queries.

Section 8: Model Context Protocol (MCP)

1. Introducing MCP: Standardizing AI-Data Connections

2. MCP Clients vs. MCP Servers

3. Defining Resources, Prompts, and Tools in MCP

4. The Server Inspector and Debugging

5. Lab 8: "Building an MCP Server" – Create a simple MCP server that exposes a local SQLite database or filesystem to Claude.

Section 9: Agents & Advanced Workflows

1. Workflows vs. Agents: When to use which?

2. Design Patterns: Parallelization, Chaining, and Routing

3. Environment Inspection and Self-Correction

4. Computer Use: Controlling Desktop Environments

5. Lab 9: "The Routing Agent" – Build a sophisticated workflow that routes incoming requests to different "specialist" prompts based on intent analysis.

Section 10: Claude Apps & Deployment

1. Anthropic Apps: Claude Code Setup

2. Enhancing Claude Code with Custom MCP Servers

3. Monitoring, Safety, and Content Filtering

4. Lab 10: "The Autonomous Engineer" – Combine Tool Use, RAG, and MCP into a single CLI agent capable of reading a repo, checking a database, and proposing a code change.