Introduction to AI Prompt Engineering

Location: On-Site or Online
Pricing: $1,150 per seat (6-seat minimum)
Length: 2 Days

Course Summary

Introduction to AI Prompt Engineering is a practical, hands-on course designed to teach students how to interact with large language models (LLMs) effectively and reliably through well-designed prompts.

Students learn how modern AI models behave, why prompt wording matters, and how to design prompts that produce consistent, high-quality, and safe outputs. The course emphasizes engineering discipline—clarity, constraints, validation, and iteration—rather than trial-and-error prompting.

By the end of the course, students are comfortable designing, testing, and refining prompts for real-world use cases such as analysis, summarization, automation, and structured output generation.

Course Outline

Day 1 – Foundations of Prompt Engineering and LLM Behavior

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

• 💬 Lecture: Overview of modern AI systems and LLMs

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

• 💬 Lecture: Deterministic vs probabilistic AI behavior

• ⚙️ Lab: Exploring LLM behavior using OpenAI chat interfaces

• ⚙️ Lab: Comparing outputs with different temperatures and prompts

• ⚙️ Lab: Observing hallucinations and failure modes

• 💬 Lecture: Prompt structure and clarity

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

• ⚙️ Lab: Writing clear, unambiguous prompts

• ⚙️ Lab: Improving output quality through prompt refinement

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

• 💬 Lecture: Tools and platforms for prompt experimentation

• ⚙️ Lab: Exploring public prompt examples and models on Hugging Face

• ⚙️ Lab: Evaluating prompt quality across different models

Day 2 – Advanced Prompt Patterns, Validation, and Real Use Cases

• 💬 Lecture: Advanced prompt engineering patterns

• 💬 Lecture: Step-by-step reasoning vs direct answers

• 💬 Lecture: Designing prompts for structured output

• ⚙️ Lab: Designing prompts that return structured data (JSON, tables)

• ⚙️ Lab: Constraining model output with explicit instructions

• 💬 Lecture: Reducing hallucinations and enforcing guardrails

• 💬 Lecture: Validating and evaluating AI responses

• ⚙️ Lab: Adding validation rules to prompts

• ⚙️ Lab: Detecting incomplete or incorrect AI outputs

• 💬 Lecture: Prompt iteration and versioning

• 💬 Lecture: Cost, latency, and performance considerations

• ⚙️ Lab: Comparing multiple prompt versions for accuracy and cost

• ⚙️ Lab: Logging prompts and responses for debugging

• 💬 Lecture: Real-world prompt engineering use cases

• ⚙️ Lab: Designing prompts for summarization and analysis

• ⚙️ Lab: Designing prompts for automation and decision support

• ⚙️ Lab: Building a small, reusable prompt library

Platforms & Sites Used

Throughout the course, students will work with and evaluate:

• OpenAI (chat interfaces and APIs)

• Hugging Face (models, demos, and prompt examples)

• Public prompt libraries and open AI documentation

Outcomes

Students who complete Introduction to AI Prompt Engineering will be able to:

• Explain how LLMs behave and where they fail

• Design clear, effective prompts for consistent output

• Apply zero-shot, few-shot, and role-based prompting techniques

• Generate structured and constrained AI outputs

• Evaluate and refine prompts systematically

• Apply prompt engineering best practices in real workflows