What AI Means for Developers Today

What makes it feel different is that the responses are non-deterministic (two identical calls can return different text), the failure mode is subtle (the response looks correct but contains made-up facts), and the cost model is unusual (you pay per token, not per request or per seat). This course teaches you how to handle all of that.

The first is plain rules and logic: if/else branches, validators, regex, workflow engines. This is the cheapest, most testable, and most auditable option. It works when the requirement is crisp: "reject passwords shorter than 8 characters" or "charge tax rate Y for country X."

The second is classical machine learning: models trained on labeled data to score or classify. Spam filters, fraud detection, churn prediction, recommendation engines. These need labeled data and a training pipeline, but once deployed, inference is fast (single-digit milliseconds) and cheap. If you have thousands of labeled examples and a clear classification task, this is usually the right pick.

The third is large language models (LLMs): models that generate text, code, and structured output by predicting one token at a time. ChatGPT, Claude, Gemini. These are the new tool in the stack. They need no labeled data (you write a prompt instead of training a model), they handle messy natural-language inputs, and they are flexible enough to do summarization, extraction, classification, rewriting, and code generation from a single API.

Most production systems combine all three. The LLM does the language-heavy work; rules enforce hard constraints; classical ML handles the high-volume scoring that would be too expensive or too slow for an LLM.

The shift came when instruction-tuned chat models became available as APIs. The change was not that models became conscious or that AI "solved" anything new. The change was that a developer could now write a prompt (a few sentences of English describing what to do), call an HTTP endpoint, and get back structured output in seconds. No training data. No model training. No GPU cluster.

For engineers, that changes the shape of the work:

• You design a prompt (like writing an API contract).
• You manage tokens and cost (like managing request payloads and billing).
• You add retries, timeouts, and validation (like any unreliable dependency).
• You evaluate quality over time (like monitoring any service).

AI stopped being a research project and started being an engineering discipline. That is what this course teaches.

Modules 1 through 4 build your vocabulary. What AI is (this module), how LLMs actually generate text (Module 2), how to write prompts that work reliably (Module 3), and how to call the API and manage cost (Module 4). By the end of Module 4 you will be able to build a working AI feature.

Modules 5 through 13 are about making that feature production-ready. Treating the LLM as an unreliable backend service with retries and circuit breakers (Module 5). Embeddings and vector search for retrieval (Module 6). Advanced RAG for better answers (Module 7). Agents and tool use (Module 8). The Model Context Protocol for portable tool servers (Module 9). Safety and guardrails (Module 10). Evaluation (Module 11). Open-weight models you can self-host (Module 12). And inference infrastructure for running models at scale (Module 13).

Each module links concepts in reading order. Read them front to back; each one picks up where the last left off.