Introduction to AI Engineering

Location: On-Site or Online
Pricing: $1,250 per seat (6-seat minimum)
Length: 3 Days

Course Summary

AI Engineering is a practical, hands-on course designed to teach engineers how to design, build, and operate AI-powered applications using modern large language models (LLMs) and AI platforms.

Students learn how to work with foundation models, design effective prompts, integrate AI into real applications, and evaluate results reliably. The course emphasizes engineering discipline—repeatability, validation, safety, and maintainability—rather than hype or purely theoretical machine learning.

By the end of the course, students are comfortable building AI-assisted tools and services, applying prompt engineering techniques, using public AI platforms responsibly, and understanding how AI fits into real production systems.

Course Outline

Day 1 – Foundations of AI Engineering and LLMs

• 💬 Lecture: What AI engineering is (and what it is not)

• 💬 Lecture: Overview of modern AI systems and LLMs

• 💬 Lecture: Tokens, context windows, temperature, and sampling

• 💬 Lecture: Deterministic vs probabilistic behavior in AI systems

• ⚙️ Lab: Exploring LLM behavior using OpenAI Chat interfaces

• ⚙️ Lab: Comparing outputs with different temperatures and prompts

• ⚙️ Lab: Observing hallucinations and failure modes

• 💬 Lecture: Prompt engineering fundamentals

• 💬 Lecture: Zero-shot, few-shot, and role-based prompting

• ⚙️ Lab: Writing clear, constrained prompts

• ⚙️ Lab: Improving output quality through prompt refinement

• ⚙️ Lab: Using system prompts to control tone and behavior

• 💬 Lecture: Tooling and platforms for AI engineers

• ⚙️ Lab: Exploring model hubs and examples on Hugging Face

• ⚙️ Lab: Evaluating public prompts and model demos

Day 2 – Prompt Engineering, Evaluation, and Integration

• 💬 Lecture: Advanced prompt engineering patterns

• 💬 Lecture: Chain-of-thought vs structured outputs

• 💬 Lecture: Asking for reasoning vs answers

• ⚙️ Lab: Designing prompts for step-by-step reasoning

• ⚙️ Lab: Forcing structured output (JSON, tables, schemas)

• 💬 Lecture: AI output validation and guardrails

• 💬 Lecture: Reducing hallucinations and ambiguity

• ⚙️ Lab: Adding explicit constraints to prompts

• ⚙️ Lab: Validating AI responses against expected formats

• 💬 Lecture: Using AI APIs in applications

• 💬 Lecture: Cost, latency, and reliability considerations

• ⚙️ Lab: Making API calls to an LLM

• ⚙️ Lab: Logging prompts and responses for debugging

• ⚙️ Lab: Comparing responses across multiple prompt versions

• 💬 Lecture: Ethical and responsible AI usage

• ⚙️ Lab: Identifying unsafe or biased outputs

• ⚙️ Lab: Designing prompts with safety in mind

Day 3 – Building Real AI-Powered Workflows

• 💬 Lecture: AI in real systems (assistants, copilots, automation)

• 💬 Lecture: Human-in-the-loop vs fully automated AI

• ⚙️ Lab: Designing an AI-assisted workflow

• ⚙️ Lab: Defining where humans approve or override AI output

• 💬 Lecture: Prompt versioning and maintainability

• 💬 Lecture: Testing AI systems

• ⚙️ Lab: Versioning prompts like code

• ⚙️ Lab: Regression testing AI outputs

• 💬 Lecture: Integrating AI into existing tools and pipelines

• 💬 Lecture: AI engineering anti-patterns

• ⚙️ Lab: Building a small AI-powered utility (chatbot, analyzer, helper)

• ⚙️ Lab: Combining prompts, validation, and workflow logic

• ⚙️ Lab: Reviewing reliability, cost, and failure modes

Platforms & Sites Used

Throughout the course, students will work with and evaluate:

• OpenAI (Chat & API usage)

• Hugging Face (models, datasets, demos)

• Public prompt libraries and evaluation tools

• Open documentation and API references

Outcomes

Students who complete AI Engineering will be able to:

• Explain how modern LLMs behave and where they fail

• Design effective prompts for consistent, high-quality output

• Validate and constrain AI responses safely

• Integrate AI into real applications and workflows

• Apply engineering discipline to AI systems

• Use public AI platforms responsibly and effectively