ChatGPT for Mechanical Engineers: A Practical Playbook

Mechanical engineers are increasingly using AI assistants like ChatGPT to speed up design workflows. We know this at CoLab because we’re primarily made up of mechanical engineers and do this work every day ourselves, while working alongside top companies that are leaders in design review.

The best use cases we have seen for mechanical engineers involve handling repetitive writing, brainstorming failure modes, and streamlining technical communication.

This guide will walk you through:

• The Anatomy of a Prompt: Basic to Advanced.
• Core Use Cases: Design Review, Documentation, and Communication.
• 5 Ready-to-Run Workflows: Complex "System Prompts" you can use immediately.
• Guardrails: How to use AI without compromising safety or IP.

Part 1: The Anatomy of an Engineering Prompt ^{The Anatomy of an Engineering Prompt}

Good results start with good prompts. Think of ChatGPT not as a search engine, but as a junior engineer: it needs context, constraints, and a defined output format to be useful.

1. Basic Prompting: The "Quick Start" Rules

If you want a usable answer, you must provide three things:

1. Context: Application, materials, loads, constraints, environment.
2. Deliverable: “Bullet list,” “table,” or “JSON.”
3. Scope: “Limit to top five risks,” “focus on passive cooling.”

Basic Example:

Goal: Shortlist materials for a hot gearbox part.

Prompt: "List the key material properties to consider when selecting a polymer for a gearbox component operating at 150 °C. Return a bullet list."

2. Advanced Prompting: Role-Playing & Constraints

Advanced prompts give the AI a specific "persona" or strict logical constraints. This prevents generic answers and forces the AI to "think" like a specialist.

• Role-Playing: "Act as a senior mechanical engineer reviewing a pressure vessel design. List any safety or manufacturing concerns."
• Chain of Thought: "First, list the thermal loads. Second, suggest three cooling methods. Third, compare them in a table."
• Expert Panel: "Act as a design panel of three experts (materials, structures, manufacturing). Evaluate the following drone frame design. Present findings per expert."

Part 2: Core Engineering Use Cases ^{Use Cases}

1. Design Reviews

Engineers can use ChatGPT to prepare for reviews or summarize findings. It excels at spotting "blind spots" in your logic.

The "Brainstorming" Prompt:

"Describe a bracket that supports a motor via two M8 bolts. Ask: What failure modes should I consider?"

The "Checklist" Prompt:

"Review this bolted joint design for common mistakes: bolt spacing is 2D, preload assumed 70% of yield, threads engaged 1.5D."

2. Technical Documentation

Reduce the time spent drafting reports, specs, and manuals.

The "Report Summary" Prompt:

"Here are the test results: Max temp = 85°C (acceptable). Vibration within limits. Draft a short paragraph summary for a formal report."

The "Comparison Table" Prompt:

"Compare material A (7075-T6) and material B (304SS) in a table of strength, corrosion resistance, and machinability."

3. Professional Communication

Save time writing clear, professional messages to suppliers or management.

The "Supplier RFC" Prompt:

"Write a professional email to a supplier explaining that we need stronger bolts due to an updated load requirement. Ask for lead time and cost impact."

The "Jargon Translator" Prompt:

"Explain fatigue failure in layman’s terms for a client email. Keep it encouraging but realistic."

Part 3: Five Ready-to-Run Workflows (The "Mega-Prompts") ^{Five Ready-to-Run Workflows}

These are complex prompts (System Instructions). Copy and paste the text inside the code blocks directly into ChatGPT to trigger a specific workflow.

Workflow 1: 15‑Minute Design‑Review Prep

Use this to quickly identify risks before walking into a review.

Markdown

# Role and Objective
Act as a senior design reviewer. Evaluate the provided engineering context and identify potential concerns.

# Instructions
1. Analyze the component based on the context provided (material, dimensions, loads, environment).
2. Identify up to 10 issues, classifying each by Severity (Critical, Moderate, Minor) and Category (strength, fatigue, stiffness, manufacturability, assembly).
3. Prioritize issues by Severity.

# Output Format
Return a single Markdown table.
Table schema: | Severity | Category | Issue | Recommendation |


Workflow 2: Revision Comparison (Delta Summary)

Use this to summarize changes between Rev A and Rev B for a release note.

Markdown

# Role and Objective
Evaluate two provided revision summaries (Rev A and Rev B) to generate a structured comparison and risk analysis.

# Instructions
1. Compare Rev A and Rev B to identify all differences and the rationale for each.
2. Present a table cataloging issues by Severity, Category, and Priority.
3. Conclude with a "Delta Summary" list: Differences, Risks Introduced, and Validation Steps.

# Output Format
Markdown table followed by a bulleted list.


Workflow 3: Test Report Digest

Use this to turn raw lab notes into a polished summary table.

Markdown

# Purpose
Summarize and tabulate test report results for documentation.

# Instructions
1. Read the provided test result bullet points (e.g., "Max temp 85°C OK").
2. Write a concise 5–7 sentence summary of the outcomes.
3. Generate a table listing Pass/Fail outcomes for each requirement ID.

# Handling Errors
If a requirement ID is missing or ambiguous, mark the Result as 'Fail' and note the ambiguity.

# Output Format
Plaintext summary followed by a Markdown table: | Requirement | Result | Notes |


Workflow 4: Supplier Request for Change (RFC) Generator

Use this to generate consistent, professional RFQs.

Markdown

# Role
Act as a procurement engineer. Compose a professional email to [supplier] regarding a specification change.

# Inputs Required
- Current spec: [X]
- Proposed spec: [Y]
- Reason: [Value]
- Deadline: [Date]

# Instructions
Generate an email asking for lead time and cost difference.
If any required input fields are missing in my prompt, output a JSON error message identifying the missing field.


Workflow 5: Executive Brief + Q&A Prep

Use this to prep for a high-stakes meeting with leadership.

Markdown

# Task
Write a one-paragraph status update based on the technical notes provided.
After the status, generate exactly 3 likely executive questions regarding Cost, Risk, and Schedule, and provide a concise answer for each.

# Output Format
Provide the output as a clean JSON object with fields: "status" and "executive_questions" (array of objects with "question" and "answer").


Part 4: Guardrails & Best Practices ^{Best Practices}

AI is a tool, not a sign-off authority. Mechanical engineering requires determinism; AI is probabilistic.

• Validate Math: AI models struggle with complex arithmetic. Always verify equations and calculations yourself.
• Be Explicit with Units: "Stress of 200" is dangerous. "Stress of 200 MPa" is clear.
• Privacy & IP: Never paste sensitive CAD geometry, trade secrets, or proprietary pricing into a public LLM. Abstract the details (e.g., "a generic aluminum bracket" rather than "Part Number X-99 for Project Apollo").
• Determinism: If you need the exact same answer every time, force a structured output (like a table or JSON) and reuse the exact same prompt string.