AI Engineering 2026: ChatGPT, RAG & Agentic Systems

Build AI Agents, Production RAG Pipelines & Hybrid LLM Systems with Python and Local + Cloud Models

What you’ll learn
✓ Build AI Agents and LLM-powered applications
✓ Design hybrid AI systems combining local and cloud models
✓ Evaluate AI frameworks architecturally rather than relying on hype
✓ Apply AI Engineering principles for scalability and long-term ownership

Requirements
● To get the most out of this course, you should have: Basic Python knowledge (functions, loops, simple scripts) Comfort using a terminal (PowerShell, Bash, or macOS Terminal) A basic understanding of APIs and HTTP requests A modern laptop (16GB RAM recommended for running local models)
● Recommended (but not required): Ability to run a Linux environment (WSL on Windows or a lightweight VM) Familiarity with JSON and structured data Curiosity about how AI systems work beyond prompt engineering
● You do not need: Prior machine learning or deep learning experience Advanced math or statistics Enterprise DevOps background Previous experience with AI agents

Description
AI Engineering in 2026 is no longer just about prompts — it’s about building AI Agents, RAG pipelines, and production-ready LLM systems.

This course is designed to be hands-on. Instead of just explaining AI concepts, we’re going to install tools, run models locally, and experiment with the systems that power modern AI engineering.

In this course, you’ll move from using tools like ChatGPT to engineering real AI architectures with agents, RAG, structured outputs, and hybrid routing that combine local models, cloud APIs, RAG pipelines, and agentic workflows.

You’ll start by running your own local LLM and validating exactly how it communicates. From there, you’ll build a simple AI assistant and then progressively evolve it into a structured, observable system.

You’ll learn how to

• Build AI Agents with controlled execution loops

• Implement reliable RAG (Retrieval-Augmented Generation) pipelines

• Enforce deterministic outputs using structured schemas

• Separate interface, engine, routing, and memory into clear architectural layers

• Design hybrid AI systems that combine local and cloud models

• Evaluate AI frameworks based on system design rather than hype

This course is designed as a practical AI engineering course for developers who want to understand what happens between “prompt” and “production” in real-world systems.

If you’ve experimented with ChatGPT or LLM APIs and want to move toward building scalable, production-ready AI systems with confidence and clarity, this course is for you.

By the end, you won’t just be using AI tools — you’ll be designing reliable, observable, production-ready AI systems you actually understand.

Who this course is for
■ AI-curious developers who can write basic Python and want to move past “prompting” into actually building.
■ Software engineers who want a fast, practical on-ramp to local + hybrid LLM workflows (Ollama + API boundaries) without needing ML math.
■ Builders and technical creators who want to ship AI features and understand what’s happening (models, endpoints, context), not just click buttons.
■ Security-aware / architecture-minded folks who want visibility and control while they learn (without this turning into a security certification course).

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