Machine Learning
AI Engineering
A hands-on AI engineering course - go from your first LLM API call to production RAG pipelines, tool-using agents, evaluation, safety, and MLOps. 18 modules plus a Python refresher.
AI Engineering
This is the course for engineers who want to build real AI products, not just call an API once. Across 18 modules you go from your very first LLM call to production systems: how LLMs actually work, prompt and context engineering, embeddings and vector search, retrieval-augmented generation (RAG) end to end, function calling and tool use, the Model Context Protocol, fine-tuning, autonomous agents, AI system architecture, evaluation and testing, safety and guardrails, deployment and MLOps, cost engineering, and multimodal generative AI.
Every module is grounded in what production AI engineering actually requires - the economics of an LLM call, retrieval quality, evaluation you can trust, guardrails against prompt injection, and the operational depth (observability, cost, latency) that separates a demo from a shipped product. A Python for AI Engineering appendix is included if you need the refresher.
The first two modules - the AI Engineering landscape and Your First AI Application - are free to preview. The full course is a one-time purchase with lifetime access.
What you will learn
- Call LLMs properly - parameters, structured output, streaming, and conversation memory
- How LLMs work under the hood: tokenisation, transformers, generation, and model selection
- Prompt and context engineering, prompt templates, and defending against prompt injection
- Embeddings, vector databases, and building semantic search that scales
- Production RAG: retrieval, citations, conversational, GraphRAG, multimodal, and agentic RAG
- Function calling, multi-tool orchestration, and the Model Context Protocol (MCP)
- Building autonomous AI agents and designing AI system architecture
- Evaluation and testing, AI safety and guardrails, deployment and MLOps
- LLM cost and latency optimisation, plus multimodal and generative AI
Prerequisites
- Comfortable programming in any language
- Basic Python helps - a "Python for AI Engineering" refresher appendix is included
Course outline
Module 1: Course Introduction and the AI Engineering Landscape
Course Introduction and the AI Engineering Landscape - 4 topics.
- Why I Built This Course 11 min read
- What AI Engineering Actually Is 11 min read
- The Modern AI Landscape 10 min read
- Your Learning Roadmap 12 min read
Module 2: Your First AI Application
Your First AI Application - 5 topics.
- Making Your First LLM API Call 12 min read
- Understanding LLM Parameters 10 min read
- Structured Output from LLMs 11 min read
- Streaming and Real-Time Responses 11 min read Pro lesson
- Conversation Management and Memory 11 min read Pro lesson
Module 3: How LLMs Work
How LLMs Work - 6 topics.
- What Are Large Language Models? 10 min read
- Tokenisation 11 min read
- The Transformer Architecture 11 min read
- How LLMs Generate Text 11 min read Pro lesson
- The LLM Training Pipeline 11 min read Pro lesson
- Model Landscape and Selection 10 min read Pro lesson
Module 4: Understanding AI Application Economics
Understanding AI Application Economics - 3 topics.
- The True Cost of an LLM Call 11 min read
- Cost Modelling for AI Applications 10 min read Pro lesson
- The Build vs Buy vs API Decision 11 min read Pro lesson
Module 5: Prompt Engineering
Prompt Engineering - 7 topics.
- What Is Prompt Engineering? 11 min read
- Anatomy of an Effective Prompt 11 min read
- Advanced Prompting Techniques 11 min read Pro lesson
- Context Engineering 11 min read Pro lesson
- Prompt Templates and Pipelines 11 min read Pro lesson
- Defending Against Prompt Injection 11 min read Pro lesson
- Prompt Optimisation and DSPy 10 min read Pro lesson
Module 6: Embeddings and Vector Search
Embeddings and Vector Search - 5 topics.
- What Are Embeddings? 11 min read
- Choosing Embedding Models 10 min read
- Vector Databases 10 min read Pro lesson
- Building a Semantic Search Engine 11 min read Pro lesson
- Scaling Vector Search in Production 12 min read Pro lesson
Module 7: RAG and Retrieval Systems
RAG and Retrieval Systems - 9 topics.
- What Is RAG? 11 min read
- Building a Production RAG Pipeline 11 min read Pro lesson
- Advanced Retrieval Techniques 11 min read Pro lesson
- RAG with Citations and Grounding 11 min read Pro lesson
- Conversational RAG 11 min read Pro lesson
- GraphRAG and Knowledge Graphs 10 min read Pro lesson
- Multimodal RAG 11 min read Pro lesson
- Agentic RAG 11 min read
- Evaluating RAG Systems 12 min read Pro lesson
Module 8: Function Calling and Tool Use
Function Calling and Tool Use - 4 topics.
- From Text Generation to Action 11 min read
- Designing Tool Schemas 10 min read Pro lesson
- Multi-Tool Orchestration 11 min read Pro lesson
- Structured Workflows with Tool Use 12 min read Pro lesson
Module 9: Model Context Protocol (MCP)
Model Context Protocol (MCP) - 4 topics.
- What Is MCP? 10 min read
- Building MCP Servers 11 min read Pro lesson
- Building MCP Clients 11 min read Pro lesson
- Production MCP Server Patterns 11 min read Pro lesson
Module 10: Fine-Tuning and Model Customisation
Fine-Tuning and Model Customisation - 6 topics.
- When to Fine-Tune (and When Not To) 11 min read
- Preparing Training Data 11 min read Pro lesson
- Supervised Fine-Tuning 11 min read Pro lesson
- PEFT, LoRA, and QLoRA 11 min read Pro lesson
- Evaluating Fine-Tuned Models 11 min read Pro lesson
- Deploying Fine-Tuned Models 11 min read Pro lesson
Module 11: Building AI Agents
Building AI Agents - 10 topics.
- What Are AI Agents? 11 min read
- Agent Memory Systems 11 min read Pro lesson
- Agent Frameworks 10 min read Pro lesson
- Multi-Agent Systems 11 min read Pro lesson
- Agent Reliability and Debugging 10 min read Pro lesson
- Human-in-the-Loop Patterns 10 min read Pro lesson
- Coding Agents and Computer Use Agents 10 min read Pro lesson
- Deep Research and Long-Running Agents 10 min read Pro lesson
- Agent Evaluation and Testing 11 min read Pro lesson
- Agent Security and Sandboxing 11 min read Pro lesson
Module 12: AI System Architecture
AI System Architecture - 8 topics.
- Architecture Patterns for AI Applications 9 min read
- Designing for Reliability 10 min read Pro lesson
- Data Architecture for AI Applications 9 min read Pro lesson
- Scaling AI Applications 9 min read Pro lesson
- The AI Gateway Pattern 12 min read Pro lesson
- Caching Strategies for LLM Applications 12 min read Pro lesson
- Building AI-Powered UX 13 min read Pro lesson
- Rate Limiting and API Management 13 min read Pro lesson
Module 13: AI Product Thinking and Design
AI Product Thinking and Design - 4 topics.
- When to Use AI (and When Not To) 14 min read
- Scoping AI Features 12 min read Pro lesson
- Designing for Probabilistic Systems 12 min read Pro lesson
- Building AI Workflows vs AI Features 13 min read Pro lesson
Module 14: Evaluation and Testing for AI Applications
Evaluation and Testing for AI Applications - 7 topics.
- Why AI Evaluation Is Different 11 min read
- Building Evaluation Datasets 12 min read Pro lesson
- Offline Evaluation Frameworks 11 min read Pro lesson
- LLM-as-Judge Evaluation 12 min read Pro lesson
- Human Evaluation 11 min read Pro lesson
- Online Evaluation and A/B Testing 10 min read Pro lesson
- Continuous Evaluation in Production 10 min read Pro lesson
Module 15: AI Safety, Guardrails, and Responsible AI
AI Safety, Guardrails, and Responsible AI - 6 topics.
- The AI Safety Landscape 12 min read
- Content Filtering and Output Validation 11 min read Pro lesson
- Hallucination Detection and Mitigation 10 min read Pro lesson
- PII Detection and Data Privacy 11 min read Pro lesson
- Red Teaming AI Applications 10 min read Pro lesson
- Building Guardrail Systems 11 min read Pro lesson
Module 16: Production Deployment and MLOps
Production Deployment and MLOps - 6 topics.
- Containerising AI Applications 10 min read
- CI/CD for AI Applications 10 min read Pro lesson
- Monitoring and Observability 10 min read Pro lesson
- Handling Production Incidents 10 min read Pro lesson
- Iterating in Production 10 min read Pro lesson
- The AI Engineering Lifecycle 10 min read Pro lesson
Module 17: LLM Optimisation and Cost Engineering
LLM Optimisation and Cost Engineering - 5 topics.
- Prompt Optimisation for Cost and Latency 10 min read
- Model Selection and Routing 10 min read Pro lesson
- Latency Optimisation 11 min read Pro lesson
- Self-Hosting Open-Source Models 11 min read Pro lesson
- Advanced Cost Engineering 11 min read Pro lesson
Module 18: Multimodal and Generative AI
Multimodal and Generative AI - 5 topics.
- Vision Models and Image Understanding 11 min read
- Text-to-Image Generation 11 min read Pro lesson
- Audio and Speech 10 min read Pro lesson
- Video Understanding and Generation 10 min read Pro lesson
- Building Multimodal Applications 11 min read Pro lesson
Module 19: Appendix - Python for AI Engineering
Appendix - Python for AI Engineering - 6 topics.