AI for Developers — Build with LLMs, RAG & Agents

You don’t need a PhD in machine learning to build with AI. You need to know how to call an API, structure a prompt, feed it the right context, and handle the response. That’s programming — not data science.

This crash course teaches you to build AI-powered applications as a software engineer. No neural network math. No training loops. Just practical patterns for integrating LLMs into real software.

What This Course Covers

Six lessons that take you from “I’ve used ChatGPT” to “I ship AI features in production”:

1. LLMs for Programmers — How AI APIs Work — What tokens are, how completions work, the request/response lifecycle, and the mental model you need
2. Calling LLM APIs — OpenAI, Anthropic & Beyond — Hands-on API calls, streaming, structured outputs, function calling, and error handling
3. Prompt Engineering for Developers — System prompts, few-shot examples, chain-of-thought, templates, and why prompt engineering is software engineering
4. RAG — Retrieval Augmented Generation — Embeddings, vector databases, chunking strategies, and giving your AI a knowledge base
5. LangChain & Orchestration Frameworks — Chains, agents, tools, memory, and when frameworks help vs. when they hurt
6. Choosing Models & Building Production AI — Model comparison, cost/quality tradeoffs, caching, evaluation, guardrails, and shipping safely

Who Is This For?

• Backend/frontend developers who want to add AI features to their apps
• Engineers evaluating LLM integration for their products
• Programmers who’ve used ChatGPT but never called an LLM API from code
• Anyone tired of AI tutorials that start with “first, let’s train a neural network”

Prerequisites

• Comfortable writing Python (we use Python throughout, but concepts apply to any language)
• Basic understanding of REST APIs and JSON
• A free-tier API key from OpenAI or Anthropic (we’ll show you how to get one)