• Build AI-powered features into your apps using OpenAI APIs and ChatGPT models – without guessing, copy-pasting random prompts, or fighting vague examples.
• This is a hands-on, demo-first course for developers who want to go beyond the ChatGPT website and actually ship real AI applications using Python and javascript.
• You’ll follow along as we build small, focused projects that mirror real-world use cases: prompt engineering, retrieval-augmented generation (RAG), fine-tuning, agents and tools, chat UIs, testing, monitoring, cost control, and more. Every demo comes with a downloadable ZIP (no GitHub required) so you can run the code locally and adapt it to your own stack.
• What you’ll do in this course

By the end, you’ll be able to:

• Call OpenAI ChatGPT models from your own backend using Python (FastAPI) and Node/Express

• Design effective prompts for explanations, summaries, code generation, and validation

• Build RAG pipelines with local documents, embeddings, and FAISS for smarter question-answering

• Use output schemas and parsers to get reliable JSON and structured data back from the model

• Set up prompt pipelines and automated tests so you can safely improve prompts over time

• Prepare data and run a small fine-tune to align a model with your product or domain

• Build web & mobile chat UIs with streaming, markdown/code rendering, and conversation state

• Orchestrate agents and tools (like a code-exec tool with sandboxed tests and safety checks)

• Add testing, monitoring, logging, and evaluation to your LLM endpoints

• Control costs, scaling, and rate limits using batching, autoscaling simulations, and throttling

• Implement security and privacy guardrails: prompt injection defenses, sanitization, and redaction

• Explore advanced topics like multimodal (image + text), FAISS sharding, and on-device inference