Introduction to LLMs and Prompt Engineering
3-Day Intensive Course for Educators, Professionals & AI Beginners
3 Intense Days
7 Hours per Day (Split into two 3.5-hour sessions)
Learning Path Visual
From understanding language models to prompting with purpose:
Day 1: Understanding LLMs and Generative AI Basics
Demystify Large Language Models (LLMs): how they’re trained, how they generate responses, and what makes them powerful. Learn key concepts like tokens, context windows, embeddings, and the importance of data quality.
Day 2: Core Prompting Techniques and Use Cases
Explore prompt formats (zero-shot, few-shot, chain-of-thought) and apply them to common scenarios — from writing support and summarization to translation, idea generation, and data interpretation.
Day 3: Building Prompt Workflows and Evaluating Output
Design multi-step prompt workflows for real-world use. Learn how to iterate and refine prompts, reduce hallucinations, improve accuracy, and evaluate LLM output for tone, logic, and reliability.
Course Overview
This beginner-friendly course introduces the core principles of Large Language Models and the practical skill of prompt engineering. Ideal for educators, entrepreneurs, content creators, and professionals, the course teaches how to design effective prompts that unlock the full power of AI — responsibly and creatively.
You’ll learn to build reusable prompt templates, troubleshoot errors, and develop AI-enhanced workflows you can use immediately in your daily work.
You’ll learn how to:
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Explain what LLMs are and how they generate responses
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Write clear, structured prompts for specific outcomes
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Use temperature, role definition, and formatting strategies
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Apply prompt engineering in education, writing, customer support, and brainstorming
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Evaluate the strengths, risks, and limitations of LLMs
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Create multi-step prompt flows for productivity and automation
What’s Inside Each Day
Day 1 — Understanding LLMs and Generative AI Basics
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How LLMs work: tokens, training, context
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What makes a “good” model output
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Generative AI vs traditional NLP
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Understanding system roles and prompt structures
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Workshop: Compare outputs from different model instructions
Toolkit: LLM vocabulary guide + model comparison worksheet
Focus: Model Literacy • Input Awareness • Fundamentals
Day 2 — Core Prompting Techniques and Use Cases
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Zero-shot vs few-shot prompting
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Prompt modifiers: tone, format, voice, role
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Prompting for clarity, creativity, summaries, rewriting
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Common use cases: lesson plans, outreach drafts, decision trees
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Workshop: Build and test a personal prompt library
Toolkit: Prompt builder template + role card deck
Focus: Prompt Structure • Context Framing • Use-Case Fit
Day 3 — Building Prompt Workflows and Evaluating Output
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Prompt chaining: step-by-step logic and structured queries
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Prompt testing: reliability, tone, hallucination detection
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Output formatting: JSON, tables, forms, instructional voice
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Ethics: bias, safety, and user responsibility
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Workshop: Design and refine a multi-step workflow
Toolkit: Evaluation rubric + prompt chain planner
Focus: Output Quality • Iteration • Ethics
Course Goals
By the end of this course, you’ll be able to:
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Use LLMs confidently across a range of everyday tasks
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Write and refine prompts that produce useful, consistent outputs
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Identify when and how to guide LLMs with structure and role setting
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Evaluate LLM responses critically and ethically
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Build shareable, reusable prompt sets for your team or projects
Who Should Take This Course?
This course is for IT professionals, managers, and analysts who need to understand and manage cybersecurity risks in a business and compliance context.
Class Reference: HIRE/ENG
Form Updated on: 06/19/2025 (Version 1)
Last Modified on: 06/19/2025
Program Note
Participants will receive downloadable templates for interview planning, competency maps, scorecards, behavioral questions, and a complete post-interview decision-making toolkit.
