Generative AI for Builders
Most AI courses teach you to use a chatbox. This one teaches you to build with AI. You will leave understanding how language models actually work at the level that lets you predict their behaviour, master Claude-specific features that most builders never discover, use Claude Code as a professional development partner, call the Anthropic API with streaming and caching, build agents that take real actions, and deploy AI in production with proper evaluation and cost control. Based on how Anthropic trains its own engineers — and the gaps they leave unfilled.
What you'll learn
Course outline
Free — no account needed
Tokens, Context Windows, and How LLMs Actually Think
The mechanics behind why AI behaves the way it does — and what this means for every prompt you write
The Model Landscape — Choosing the Right AI for Every Job
Claude, GPT, Gemini, Llama — the intelligence/cost/speed triangle and how to navigate it
Prompt Architecture — The Framework That Works Every Time
System prompts, XML tags, chain of thought, structured output — the professional's prompting toolkit
Full course — $79 one-time
Claude-Specific Features Most Builders Never Find
Extended thinking, Projects, Artifacts, computer use, and the model behaviours that set Claude apart
Claude Code — AI Development at the Command Line
CLAUDE.md, slash commands, multi-file context, permission modes, and the professional workflow
Claude Code — MCP Servers, Hooks, and Automated Workflows
Connect Claude Code to any tool, trigger automations on AI events, and build team-shared workflows
Tool Use — Giving AI the Ability to Act
How tool use works, defining tools, building the tool loop, and when to use agents vs structured output
The Claude API — Streaming, Caching, Batching, and Cost Control
The API features that cut costs by 90% and latency by 60% — that most developers never enable
Multimodal — Vision, Documents, and the Files API
What Claude can see, how to prompt for images, PDF analysis, and the Files API for persistent uploads
Evaluating AI Output — Evals, Testing, and LLM-as-Judge
How to detect regressions before users do and build confidence in AI-powered features
Building AI Agents — Multi-Step Workflows That Plan and Self-Correct
The architecture of agents that actually work in production — orchestration, memory, and graceful failure
Production AI Systems — Cost, Latency, Safety, and the Pre-Launch Checklist
The operational decisions that separate prototype from production — and the checklist before you go live
Get the full course
12 lessons — from tokens and context windows to production AI agents with eval pipelines.
About this course
Generative AI has moved from novelty to core infrastructure in two years — and developers who understand how to build with it have a significant advantage. This generative AI tutorial for builders covers how to integrate large language models into real applications: API usage, prompt design, streaming responses, tool calling, structured output, and cost management. You will learn using real code examples rather than high-level theory, building features you can ship immediately.
This course is for developers who want to add AI capabilities to their products and founders who need to understand what AI can and cannot do. After completing it you will be able to integrate Claude, GPT-4o, or any major LLM API into a web application, design prompts that produce reliable results, handle edge cases gracefully, and make informed decisions about which AI features are worth building.
Frequently asked questions
Do I need a machine learning background to build with generative AI?
No — building applications with generative AI APIs requires software development skills, not machine learning expertise. You are integrating an API, not training a model. The key skills are understanding how to structure prompts, handle streaming responses, manage API errors, and design UX around AI outputs that are probabilistic rather than deterministic. Standard web development skills are sufficient to get started.
Which AI APIs does this course cover?
This course focuses on the Anthropic Claude API and OpenAI API, as they are the most widely used in production applications. The patterns — streaming, tool calling, structured output, system prompts — are nearly identical across providers, so skills transfer directly to Gemini, Mistral, or any other API. We also cover the Vercel AI SDK, which provides a unified interface for multiple providers in Next.js applications.
What is tool calling and why does it matter?
Tool calling (also called function calling) is a capability where you define functions that the AI model can request to run — like searching a database, calling an external API, or performing a calculation. The model determines when to call a tool, what arguments to pass, and incorporates the result into its response. Tool calling transforms AI from a text generator into an agent that can take actions — the foundation for most sophisticated AI product features.
How do I manage AI API costs in a production application?
API costs are driven by token usage — both the tokens you send in and the tokens the model generates. Practical cost management: cache common responses, use smaller models for simpler tasks, truncate conversation history rather than sending the full context, stream to reduce perceived latency without increasing cost, and monitor per-user token consumption. This course covers cost management patterns so your AI features remain economically viable at scale.
What is the difference between using an AI chat tool and building with the API?
Chat tools like Claude.ai are consumer interfaces — you type, it responds. Building with the API means your application controls the conversation programmatically: setting a system prompt that defines the AI behaviour, managing conversation history, parsing structured outputs, calling tools, and streaming responses to your own UI. The API gives you full control over the AI experience inside your product.