Write Better Engineering Prompts with CRISPE
- Patrick Law
- May 6
- 2 min read
AI won’t solve your engineering problems—unless you tell it exactly what you need. The CRISPE method helps technical professionals write high-quality prompts that get precise, usable results from AI.
Why CRISPE Works for Engineers
The CRISPE framework gives structure to your prompts so AI tools like ChatGPT, Claude, and Gemini can produce high-accuracy technical outputs. Here's why engineers are adopting it fast:
Clear Context: Helps AI understand the background of the problem (e.g., piping spec, system constraints).
Defined Role: Assigns a persona like “process engineer” or “QA reviewer,” improving tone and focus.
Complete Input: Ensures you include flow rates, standards (API, ASME), units, and assumptions.
Step-by-Step Logic: Guides the AI through the desired workflow—calculating, checking, or summarizing.
Focused Purpose: Sets the specific goal, like sizing a vessel or validating pressure drop.
Structured Output: Requests exactly what you need—tables, citations, or code—eliminating guesswork.
Used together, these elements create prompts that save hours of rework and align with real-world engineering workflows.
What CRISPE Won’t Do
CRISPE doesn’t guarantee results if you feed it incorrect or vague data. It's a framework, not a substitute for engineering judgment.
Limitations include:
It depends heavily on accurate inputs and reference standards.
It still requires a qualified engineer to review and validate the AI’s outputs.
Not all AI tools interpret complex formulas or standards equally well.
For high-stakes work, tools like Perplexity.ai or plugins tied to API standards may complement CRISPE by enhancing factual depth and citation accuracy.
Get Started with Better Prompts
If you're using AI in engineering—and want results that meet technical standards—start using the CRISPE method today. It’s fast, structured, and proven to reduce errors in calculations, documentation, and design reviews.
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