This Is Why Engineers Hate GPT—And What They’re Using Instead
This Is Why Engineers Hate GPT—And What They’re Using Instead
This Is Why Engineers Hate GPT—And What They’re Using Instead
Dr. Maor Farid
This Is Why Engineers Hate GPT—And What They’re Using Instead
If you're a mechanical engineer, you’ve probably tried using ChatGPT. Maybe it was for a quick material recommendation, help writing a technical spec, or even assistance with a drawing description. The output looked clean, confident, and fast. But something felt off.
That feeling is justified—because ChatGPT wasn’t built for engineers.
In fact, across every professional field, we’re seeing a shift away from generic AI tools like ChatGPT toward domain-specific AI copilots designed to meet the unique needs of each profession. Just look at what’s happening in other industries:
Software engineers use GitHub Copilot and Codium to autocomplete code, refactor functions, and spot bugs—all within their IDEs.
Lawyers are turning to Harvey, a legal AI assistant trained only on legal documents and integrated directly into platforms like Microsoft Word.
Video editors use Runway or Pika Labs to generate and edit video content through models that understand visual structure and motion in ways GPT never could.
Doctors and medical researchers use tools like Glass AI or Syntegra that are tailored to the language of medical diagnosis, research papers, and EHR data.
Marketers use tools like Jasper or Copy.ai, built specifically for brand-safe copy, campaign planning, and SEO strategies.
Sales teams rely on AI assistants like Regie.ai and Apollo AI to personalize outreach and write sales emails that align with buyer intent.
These tools aren’t necessarily smarter than GPT. In fact, many are built on GPT under the hood. But they’re winning in their markets because they deliver real value by being tightly focused on one domain.
The key lies in four critical differentiators that make these tools suitable for professionals:
Workflow Integration – GitHub Copilot integrates into code editors like VSCode; Harvey works inside Word. These tools appear where professionals already work.
Reliability – Unlike GPT, which was trained to satisfy general audiences, domain-specific tools rely on curated, high-fidelity data. Codium uses vetted code repositories; Harvey is trained on legal precedents, not web forums.
Modality Alignment – Tools like Runway.ai understand video frames, motion, and visual context. You can’t do that with a text-only model like GPT.
IP Protection – Professionals care deeply about intellectual property. That’s why tools like Harvey, Codium, and others are built with strict data privacy standards. GPT, by contrast, was created as a general consumer tool, not a secure enterprise solution.
So if you’re a mechanical engineer using ChatGPT, you’re essentially doing what a lawyer would do with Wikipedia or what a programmer would do with Notepad.
There’s now an AI copilot built for you—Leo.
The rest of this post explains why ChatGPT falls short in engineering design, and how Leo fills the gap with source-backed answers, workflow-aware context, CAD understanding, and enterprise-grade privacy.
Here’s a clear breakdown of why ChatGPT is not built for professional engineering work—and what engineers are starting to use instead.
Generalist vs. Specialist
ChatGPT is a general-purpose model. It was trained on nearly everything published on the internet—scientific papers, blog posts, Reddit threads, and high school essays. Its goal is to produce responses that sound good, regardless of whether they are technically accurate.
In contrast, Leo is an AI copilot built specifically for mechanical engineering. It was trained on over one million engineering-grade sources: textbooks, standards, manufacturer datasheets, technical manuals, and more. It was not built to entertain—it was built to inform.
Reliability and Source Transparency
The single most important requirement in engineering decision-making is reliability. ChatGPT doesn’t offer it. It doesn’t cite sources. It doesn’t tell you when it’s guessing. And it’s not designed to distinguish between an ISO standard and a blog post written by a non-expert.
Leo was designed with source transparency at its core. Every answer is backed by engineering-grade references. If Leo cannot find a high-confidence answer in its library, it clearly informs the user. This separation between sourced knowledge and general inference gives engineers confidence in every answer they receive.
CAD and Workflow Integration
ChatGPT operates in a vacuum. It doesn’t understand your CAD assemblies, your folder structures, your part files, or your internal guidelines. It cannot see what you're designing, let alone understand the engineering context behind it.
Leo, by contrast, integrates directly into your existing engineering workflows. It connects to your Windows folders, CAD editor, and PDM systems. It understands the geometry you're working on and leverages your company’s tribal knowledge—internal documents, past projects, and legacy standards—to give context-aware answers that reflect your actual work.
Engineering-Grade Multimodality
ChatGPT processes text. It cannot reason over geometry, physics, or mechanical constraints. That’s a critical gap when working with real-world products.
Leo uses a purpose-built Large Mechanical Model (LMM) that understands CAD geometry, physical relationships between parts, and the constraints engineers work with daily. It doesn't just parse text—it reasons across modalities, combining geometric data and technical documents to deliver relevant and manufacturable suggestions.
Data Privacy and IP Protection
In mechanical design, intellectual property is everything. ChatGPT stores user data and uses it to train future models. That means your questions—and potentially your proprietary knowledge—could help competitors in future responses.
Leo was built with enterprise-grade data protection. When you subscribe to Leo, a secure instance of the AI model is deployed just for you. Your data never leaves your environment, is never used to train models, and is never accessible to other users. If you leave the platform, your model instance is deleted along with its data representation.
✅ Comparison Table – Leo vs GPT
Capability | GPT (Generic AI) | Leo (Engineering Copilot) |
Domain-specific for mechanical engineers | ❌ General-purpose model | ✅ Built by and for engineers |
Reliable, high-fidelity sources | ❌ Trained on blogs + forums, no source traceability | ✅ Trained on 1M+ verified engineering sources, shows references |
Knows what it doesn’t know | ❌ Often guesses without warning | ✅ Alerts user when unsure, separates web vs source-based answers |
Integrated into CAD & file systems | ❌ No integration | ✅ Embedded into Windows folders, CAD tools, PDMs |
Understands CAD geometry & constraints | ❌ Text-only model | ✅ Uses LMM (Large Mechanical Model) to understand design context |
Engineering-specific multimodal reasoning | ❌ Doesn’t understand geometry | ✅ Combines geometry, physics, and text to generate accurate answers |
Protects your IP & data | ❌ Data may be used to train models | ✅ Your data stays private, never used to train models |
Enterprise-grade data separation | ❌ Shared model for all users | ✅ Dedicated clone deployed securely per customer |
✔️ Simple Visual Matrix (Alt Version)
Capability | GPT | Leo |
General AI for everyone | ✔️ | ❌ |
Built specifically for engineers | ❌ | ✔️ |
CAD + geometry understanding | ❌ | ✔️ |
Uses engineering sources only | ❌ | ✔️ |
Links to source documents | ❌ | ✔️ |
Understands your file system | ❌ | ✔️ |
Uses your private CAD & docs | ❌ | ✔️ |
Knows when it's uncertain | ❌ | ✔️ |
Data kept 100% private | ❌ | ✔️ |
🧠 TL;DR Table – One-liner Comparison
GPT | Leo |
General-purpose AI, trained on everything | Mechanical engineering AI, trained on trusted sources |
Sounds confident, but not always correct | Links to engineering sources, Warns when can’t base his answers on sources |
Doesn’t understand your CAD | Understands your files and design context |
Uses your data to train itself | Never trains on your data, fully private |
Summary
ChatGPT is an impressive tool for general knowledge. But when it comes to engineering design, it lacks the depth, context, and reliability required by professional mechanical engineers.
Leo was built to close that gap. It delivers:
Answers based on engineering-grade sources
Deep understanding of CAD and design context
Integration with your real workflows
Reliable source citation and confidence-level clarity
Enterprise-grade data privacy
If your work depends on precision, safety, and performance, it’s time to use an AI designed specifically for engineering.
This Is Why Engineers Hate GPT—And What They’re Using Instead
If you're a mechanical engineer, you’ve probably tried using ChatGPT. Maybe it was for a quick material recommendation, help writing a technical spec, or even assistance with a drawing description. The output looked clean, confident, and fast. But something felt off.
That feeling is justified—because ChatGPT wasn’t built for engineers.
In fact, across every professional field, we’re seeing a shift away from generic AI tools like ChatGPT toward domain-specific AI copilots designed to meet the unique needs of each profession. Just look at what’s happening in other industries:
Software engineers use GitHub Copilot and Codium to autocomplete code, refactor functions, and spot bugs—all within their IDEs.
Lawyers are turning to Harvey, a legal AI assistant trained only on legal documents and integrated directly into platforms like Microsoft Word.
Video editors use Runway or Pika Labs to generate and edit video content through models that understand visual structure and motion in ways GPT never could.
Doctors and medical researchers use tools like Glass AI or Syntegra that are tailored to the language of medical diagnosis, research papers, and EHR data.
Marketers use tools like Jasper or Copy.ai, built specifically for brand-safe copy, campaign planning, and SEO strategies.
Sales teams rely on AI assistants like Regie.ai and Apollo AI to personalize outreach and write sales emails that align with buyer intent.
These tools aren’t necessarily smarter than GPT. In fact, many are built on GPT under the hood. But they’re winning in their markets because they deliver real value by being tightly focused on one domain.
The key lies in four critical differentiators that make these tools suitable for professionals:
Workflow Integration – GitHub Copilot integrates into code editors like VSCode; Harvey works inside Word. These tools appear where professionals already work.
Reliability – Unlike GPT, which was trained to satisfy general audiences, domain-specific tools rely on curated, high-fidelity data. Codium uses vetted code repositories; Harvey is trained on legal precedents, not web forums.
Modality Alignment – Tools like Runway.ai understand video frames, motion, and visual context. You can’t do that with a text-only model like GPT.
IP Protection – Professionals care deeply about intellectual property. That’s why tools like Harvey, Codium, and others are built with strict data privacy standards. GPT, by contrast, was created as a general consumer tool, not a secure enterprise solution.
So if you’re a mechanical engineer using ChatGPT, you’re essentially doing what a lawyer would do with Wikipedia or what a programmer would do with Notepad.
There’s now an AI copilot built for you—Leo.
The rest of this post explains why ChatGPT falls short in engineering design, and how Leo fills the gap with source-backed answers, workflow-aware context, CAD understanding, and enterprise-grade privacy.
Here’s a clear breakdown of why ChatGPT is not built for professional engineering work—and what engineers are starting to use instead.
Generalist vs. Specialist
ChatGPT is a general-purpose model. It was trained on nearly everything published on the internet—scientific papers, blog posts, Reddit threads, and high school essays. Its goal is to produce responses that sound good, regardless of whether they are technically accurate.
In contrast, Leo is an AI copilot built specifically for mechanical engineering. It was trained on over one million engineering-grade sources: textbooks, standards, manufacturer datasheets, technical manuals, and more. It was not built to entertain—it was built to inform.
Reliability and Source Transparency
The single most important requirement in engineering decision-making is reliability. ChatGPT doesn’t offer it. It doesn’t cite sources. It doesn’t tell you when it’s guessing. And it’s not designed to distinguish between an ISO standard and a blog post written by a non-expert.
Leo was designed with source transparency at its core. Every answer is backed by engineering-grade references. If Leo cannot find a high-confidence answer in its library, it clearly informs the user. This separation between sourced knowledge and general inference gives engineers confidence in every answer they receive.
CAD and Workflow Integration
ChatGPT operates in a vacuum. It doesn’t understand your CAD assemblies, your folder structures, your part files, or your internal guidelines. It cannot see what you're designing, let alone understand the engineering context behind it.
Leo, by contrast, integrates directly into your existing engineering workflows. It connects to your Windows folders, CAD editor, and PDM systems. It understands the geometry you're working on and leverages your company’s tribal knowledge—internal documents, past projects, and legacy standards—to give context-aware answers that reflect your actual work.
Engineering-Grade Multimodality
ChatGPT processes text. It cannot reason over geometry, physics, or mechanical constraints. That’s a critical gap when working with real-world products.
Leo uses a purpose-built Large Mechanical Model (LMM) that understands CAD geometry, physical relationships between parts, and the constraints engineers work with daily. It doesn't just parse text—it reasons across modalities, combining geometric data and technical documents to deliver relevant and manufacturable suggestions.
Data Privacy and IP Protection
In mechanical design, intellectual property is everything. ChatGPT stores user data and uses it to train future models. That means your questions—and potentially your proprietary knowledge—could help competitors in future responses.
Leo was built with enterprise-grade data protection. When you subscribe to Leo, a secure instance of the AI model is deployed just for you. Your data never leaves your environment, is never used to train models, and is never accessible to other users. If you leave the platform, your model instance is deleted along with its data representation.
✅ Comparison Table – Leo vs GPT
Capability | GPT (Generic AI) | Leo (Engineering Copilot) |
Domain-specific for mechanical engineers | ❌ General-purpose model | ✅ Built by and for engineers |
Reliable, high-fidelity sources | ❌ Trained on blogs + forums, no source traceability | ✅ Trained on 1M+ verified engineering sources, shows references |
Knows what it doesn’t know | ❌ Often guesses without warning | ✅ Alerts user when unsure, separates web vs source-based answers |
Integrated into CAD & file systems | ❌ No integration | ✅ Embedded into Windows folders, CAD tools, PDMs |
Understands CAD geometry & constraints | ❌ Text-only model | ✅ Uses LMM (Large Mechanical Model) to understand design context |
Engineering-specific multimodal reasoning | ❌ Doesn’t understand geometry | ✅ Combines geometry, physics, and text to generate accurate answers |
Protects your IP & data | ❌ Data may be used to train models | ✅ Your data stays private, never used to train models |
Enterprise-grade data separation | ❌ Shared model for all users | ✅ Dedicated clone deployed securely per customer |
✔️ Simple Visual Matrix (Alt Version)
Capability | GPT | Leo |
General AI for everyone | ✔️ | ❌ |
Built specifically for engineers | ❌ | ✔️ |
CAD + geometry understanding | ❌ | ✔️ |
Uses engineering sources only | ❌ | ✔️ |
Links to source documents | ❌ | ✔️ |
Understands your file system | ❌ | ✔️ |
Uses your private CAD & docs | ❌ | ✔️ |
Knows when it's uncertain | ❌ | ✔️ |
Data kept 100% private | ❌ | ✔️ |
🧠 TL;DR Table – One-liner Comparison
GPT | Leo |
General-purpose AI, trained on everything | Mechanical engineering AI, trained on trusted sources |
Sounds confident, but not always correct | Links to engineering sources, Warns when can’t base his answers on sources |
Doesn’t understand your CAD | Understands your files and design context |
Uses your data to train itself | Never trains on your data, fully private |
Summary
ChatGPT is an impressive tool for general knowledge. But when it comes to engineering design, it lacks the depth, context, and reliability required by professional mechanical engineers.
Leo was built to close that gap. It delivers:
Answers based on engineering-grade sources
Deep understanding of CAD and design context
Integration with your real workflows
Reliable source citation and confidence-level clarity
Enterprise-grade data privacy
If your work depends on precision, safety, and performance, it’s time to use an AI designed specifically for engineering.