Generative AI: From Complete Beginner to AI Engineer

Generative AI for beginners, explained in plain language.

This course gives you a complete map of generative AI. You will learn what a large language model is, how ChatGPT differs from the model behind it, and how an app sends a prompt to an AI API and gets an answer back. You will also learn the ideas behind prompt engineering, retrieval-augmented generation (RAG), AI agents, multimodal image and video models, GPUs, fine-tuning, deployment, and production security.

You do not need a coding background, a math background, an API key, or a paid AI account. Each lecture answers one practical question, shows it on a simple figure, and connects it to the same running example: a small personal assistant. By the end you will be able to follow AI discussions at work, read product claims with a cooler head, and know what a later hands-on course is actually teaching.

What you will understand

You will be able to explain how a model turns a prompt into text, what tokens and a context window are, and why an answer can sound confident and still be wrong. You will be able to tell models, providers, APIs, and applications apart. You will be able to read an AI request and name the endpoint, the JSON body, the model ID, and the credential. You will know how RAG, agents, fine-tuning, and deployment change what an application can do, and which risks matter before that app is shared.

How the course is taught

The lessons are visual and sequential. A new term is defined before it is used. Short Python commands appear only in the Python and RAG lecture, so you can see a terminal, a variable, a virtual environment, and a document split. Everywhere else, the demonstration is a figure, not a pretend live coding session. You can watch on a computer with a browser. Python 3 is optional, and only if you want to type those short examples yourself.

The 12 modules

1. Module 1. Generative AI foundations: models, tokens, and prompts

Start from zero. Learn what artificial intelligence is, how generative AI differs from traditional software, and what a large language model and an AI model are. Then learn model weights, training, inference, prompts, tokens, and the context window, and follow one path from a prompt to a response.

2. Module 2. AI models explained: ChatGPT, providers, and how to choose

Learn why ChatGPT is not the same thing as GPT, who provides a model, and whether you can download one. See where models live, what sits inside a model repository, what a size such as 70B actually means, how a large model becomes smaller, what makes a reasoning model different, how one model can handle images and audio, and how to choose a first model.

3. Module 3. AI APIs for beginners: endpoints, JSON, keys, and tokens

Learn how your app reaches a model. Cover APIs, API endpoints, HTTP requests, GET versus POST, JSON, API keys, model IDs, and OpenAI-compatible APIs. Then learn input and output tokens, plus how AI API responses and errors work. You will follow one complete request. No key and no paid provider are required.

4. Module 4. Prompt engineering basics: temperature, hallucinations, and trust

Learn what prompt engineering is, what a system instruction does, and how temperature and top p change the wording of an answer. See structured output and tool calling. Then learn why AI models hallucinate, what an embedding is, what semantic search is, and how to evaluate an AI answer before you rely on it.

5. Module 5. Python for beginners: RAG, chunking, and vector databases

Learn why so many AI tutorials use Python, what a terminal is, and what a variable, a package, and a virtual environment are. Compare an AI SDK with a raw request, see what FastAPI is for, and learn document chunking, retrieval-augmented generation (RAG), and vector databases. The commands are short beginner examples, not a full application build.

6. Module 6. AI agents explained: tools, memory, LangChain, and MCP

Learn what an AI agent is and how an agent differs from a chatbot. Follow an agent tool, the agent loop, and agent memory. Then place OpenClaw, LangChain, LangGraph, MCP, and multiagent systems on the map, so you can recognize a workflow that pauses, remembers, and uses tools.

7. Module 7. Multimodal AI: text to image, voice, and video

Leave text-only chat. Learn how AI generates images, what a diffusion model is, and how text-to-image, image-to-image, and inpainting differ. Then cover text-to-speech, speech recognition, text-to-video, image-to-video, and multimodal inference.

8. Module 8. GPUs and model serving: CUDA, Ollama, vLLM, and local AI

Learn what a GPU is, why AI workloads use GPUs, and what VRAM and CUDA mean. Compare GPU inference, local AI with Ollama, serving with vLLM, serverless AI, and an AI inference endpoint. You will know where a model runs, and what each layer is responsible for.

9. Module 9. Fine-tuning explained: LoRA, QLoRA, and training data

Learn how a general model becomes more specific. Cover pretraining, fine-tuning, training datasets, loss functions, and gradient descent. Then learn LoRA, QLoRA, instruction tuning, model distillation, and overfitting, so you can tell a training method from a product slogan.

10. Module 10. Deploy AI apps: Docker, AWS Lambda, CI/CD, and scaling

Learn the words used when an app leaves a laptop. Cover Docker, container images, container registries, cloud deployment, AWS Lambda, web functions, CI/CD, GitHub Actions, autoscaling, and load balancing.

11. Module 11. Production AI security: LLMOps, prompt injection, and governance

Learn what production care looks like. Cover LLMOps, model monitoring, AI observability, model routing, AI caching, rate limiting, and request batching. Then learn prompt injection, AI red teaming, and AI governance, including why an API key must stay private.

12. Module 12. Advanced generative AI: transformers, RLHF, and foundation models

Finish with the ideas underneath modern models. Learn what a transformer is, what attention means, and what mixture of experts, reinforcement learning, RLHF, and DPO refer to. Then cover test-time compute, world models, agentic reinforcement learning, and foundation models.

Who this course is for

This generative AI course is for beginners who want the picture before they use a pile of tools or pay for an API. It is for professionals who already chat with AI and want the meaning of models, prompts, APIs, RAG, and agents. It is for students and career switchers who want the vocabulary before a hands-on course. It is also for founders, managers, and team members who need to follow AI discussions without becoming developers first.

Take the next coding course after this one if your goal is to build and ship a RAG pipeline, an agent, or a deployed service. This course is the map those projects assume you already have.