Course Preview with Generative AI

Objective: Preview the major topics of the course by working through a set of Generative AI prompts, and practice using AI as a tutor that tests your understanding instead of doing the work for you. Estimated time: 2-3 hours.

This is the first assignment of the course, and it sets the pattern for all that follow: you direct the AI, the AI helps you learn, and you curate the evidence of what you learned into a short report. Read the Generative AI for Process Control topic page first.

Set Up Your Tools

Choose a Generative AI assistant you will use this semester (e.g., ChatGPT, Claude, Gemini, or Copilot). A free tier may not be sufficient.
Install the course TA skill in your assistant (instructions for Claude, ChatGPT/Codex, and Gemini are in the GitHub archive). It turns your AI into a course-aware TA that knows the schedule, the TCLab, the apps, and the course AI policy. The prompts below work with or without it, but the TA skill gives more course-specific coaching.
Install Python with the standard packages (matplotlib, numpy, scipy, pandas). You will write far less code than in past semesters, but Python lets you verify AI-generated results.
Bookmark the simulation and control apps used throughout the course for modeling and control exercises without heavy coding.

Step 1: Run the Topic Preview Prompts

Work through the six prompts below with your AI assistant, one topic at a time. Answer the AI's questions yourself before asking it for explanations. Copy each prompt as written, then engage in the conversation it starts.

Prompt 1 - Dynamic Behavior (Classes 2-7)

"I am a chemical engineering student starting a senior-level Process Dynamics and Control course. Act as a tutor. Using a real example (a stirred tank heater or a car's speed), teach me what it means for a process to be 'dynamic' and what a 'time constant' and 'dead time' are, without calculus. Then ask me 4 conceptual questions one at a time, wait for my answers, correct me with explanations, and end with a summary of what I should review. Do not reveal answers before I attempt them."

Prompt 2 - Balance Equations and Modeling (Classes 3-5)

"Quiz me on physics-based modeling for a senior process control course. Ask me 5 questions, one at a time, about conservation of mass and energy for a tank that is being filled and heated: what accumulates, what flows in and out, what assumptions make the model simpler, and what happens at steady state. Grade each answer, keep score, and finish by listing my misconceptions. Then show me the general balance equation form: accumulation = in - out + generation."

Prompt 3 - Feedback Control (Classes 8-11)

"Explain the difference between open-loop and closed-loop (feedback) control using a shower temperature analogy, then tell me where the analogy breaks down. Then give me an explanation of proportional, integral, and derivative control action that contains ONE subtle conceptual error. I will try to find the error. After I answer, reveal it and explain why it matters when tuning a real controller."

Prompt 4 - Sensors, Actuators, and Valves (Classes 17-18)

"Act as a senior instrumentation engineer interviewing me for an internship. Ask me 4 practical questions, one at a time, about how a temperature measurement gets from a sensor to a controller to a valve: what a transmitter does, why 4-20 mA signals are used, what valve 'fail-open' vs 'fail-closed' means and how to choose for safety, and why sensors have response lags. Correct my answers with brief explanations and give me an overall assessment at the end."

Prompt 5 - Laplace Transforms and Transfer Functions (Classes 19-22)

"I am about to learn Laplace transforms and transfer functions in a process control course. Without heavy math, explain WHY engineers transform differential equations into the Laplace domain and what a transfer function lets you do that a differential equation makes difficult. Use the analogy of logarithms turning multiplication into addition. Then ask me 3 questions to check that I understood the purpose (not the mechanics), one at a time, and correct my answers."

Prompt 6 - The Course Project (Classes 26-41)

"In the second half of my process control course I will build a control system with a microcontroller, sensors, and actuators, and I will need basic electrical engineering: circuits, sensors, actuators, motors, and electrical safety. Ask me 5 questions, one at a time, to find out what I already know about voltage, current, resistance, analog vs digital signals, and safe wiring practice. Based on my answers, give me a personalized 5-item study checklist ranked by priority."

Step 2: Test the AI's Engineering Judgment

AI assistants are confident even when wrong. Pick one of the exchanges above and push back: ask "What are the limitations of your explanation? Give a case where the rule you taught me fails." Then verify one specific claim from the conversation against the linked course pages (for example, the definition of a time constant on the First-Order Systems page). Note whether the AI was right, incomplete, or wrong.

Step 3: Plan Your Prompts for the Semester

Review the prompt-planning pages from the Machine Learning for Engineers course: Agentic Workplan, Agentic Coding, Agentic Visualization, and Agentic Reports. Write two reusable prompts of your own that you plan to use in this course: one for learning a new concept and one for checking your work. Follow the Context-Task-Constraints-Verification structure.

What to Turn In

Submit a report (PDF, about 2-3 pages) that curates what you learned. You may use Generative AI to help write and format the report, but you must guide it to include correct content, and you are responsible for every claim in it. Answer these questions:

For each of the six topic prompts: what is one thing you learned and one question you answered incorrectly (with the corrected answer)?
From Step 2: what claim did you verify, what did you find, and what does this tell you about when to trust AI output?
From Step 3: include your two reusable prompts and explain how each part (context, task, constraints, verification) improves the response.
Which AI assistant did you choose for the semester, and what is one thing you noticed about how the quality of your prompt changed the quality of its teaching?
Include a screenshot of your Python installation (pip list or a plot from any script) showing your verification toolchain is ready.