Prompt Lab Rowan University Get all three ZIP · 67.9 MB

Three-part classroom series

Prompt Engineering for Beginners

A practical path from how language models think, through how to prompt them clearly, to how agents use tools and skills.

Explore the lectures

96 slides · editable PowerPoint files

LLMs tokens + attention
Prompts clarity + context
Agents reason + act

Start here!

On today’s board

One idea builds into the next.

  1. 01 Understand How LLMs turn language into predictions
  2. 02 Communicate How precise prompts improve useful output
  3. 03 Orchestrate How agents reason, use tools, and repeat skills

Lecture notes

Pick up where you left off.

Each deck is the original editable PPTX. Open one lesson or download the complete series.

Title slide for Lecture I: Introduction to Generative AI

The foundations

Lecture I 31 slides

How models learn the language game

Follow the journey from rule-based systems to generative AI, then unpack the pieces inside modern language models: tokens, embeddings, attention, prediction, and alignment.

  • Rules → machine learning → deep learning → generative AI
  • Tokenization, embeddings, and self-attention
  • BERT vs. GPT and decoding with temperature, top-k, and top-p
  • Human preference alignment with RLHF and DPO
Download Lecture I PowerPoint · 3.5 MB
Title slide for Lecture II: Introduction to Prompt Engineering

The craft

Lecture II 30 slides

From vague asks to useful prompts

Learn how a prompt’s instruction, structure, role, examples, and context shape the answer—and how to debug a prompt when the first result misses the mark.

  • Instructional, structured, role-based, and example prompts
  • Prompt refinement, meta-prompting, and practical debugging
  • Hallucinations, verification, and giving the model an “out”
  • Context windows, memory, and retrieval-augmented generation
Download Lecture II PowerPoint · 29.5 MB
Title slide for Lecture III: Introduction to Agents

The next step

Lecture III 35 slides

When prompts learn to act

Move beyond one-shot answers into agent loops that observe, reason, act, and improve. See how ReAct, tools, reusable skills, and retrieval turn a model into a goal-driven system.

  • The observe → reason → act agent loop
  • ReAct, system prompts, tools, and iterative execution
  • Reusable agent skills for consistent decision-making
  • RAG workflows and the GUIDE-AI project challenge
Download Lecture III PowerPoint · 39.7 MB

The big idea

Better AI work starts with better mental models.

Understand the model, communicate the goal, then design the loop. These lectures give beginners the vocabulary to do all three.

Download the full series 3 PowerPoint decks · 67.9 MB ZIP
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