AI for Engineering Knowledge Management
General AI trains on Reddit and forums. Here's why that's dangerous for engineering work and what purpose-built AI does differently.
·
⏱
8 min read
Michelle Ben-David
Michelle Ben-David is a mechanical engineer and Technion graduate. She served in an IDF elite technology and intelligence unit, where she developed multidisciplinary systems integrating mechanics, electronics, and advanced algorithms. Her engineering background spans robotics, medical devices, and automotive systems.
BOTTOM LINE
General AI models train on Reddit, forums, and the open internet. That's fine for casual questions but dangerous for engineering decisions where wrong data means failed parts and costly recalls. Purpose-built engineering AI trained on vetted technical sources, with citations on every answer, gives engineers the accuracy and traceability their work demands. Leo AI's Large Mechanical Model is built on over one million verified engineering references, and it shows its sources every time.
You ask ChatGPT for the yield strength of 17-4 PH stainless steel in Condition H900. It gives you a number. Sounds confident. But where did that number come from? A Reddit thread? A hobbyist forum post from 2014? A blog that copied from another blog that paraphrased a datasheet incorrectly?
This is the core problem with using general AI for engineering work. The models are impressive, no question. But the data they trained on is a mess of professional knowledge, amateur speculation, outdated references, and flat-out wrong information, all blended together with no way to tell which is which.
For software engineering, where answers can be validated instantly by running code, that's manageable. For mechanical engineering, where a wrong material property or tolerance can result in a failed part, a recalled product, or a safety incident, it's a real risk.
What General AI Actually Trains On
Large language models like ChatGPT and Claude are trained on massive datasets scraped from the internet. That includes Reddit, Stack Exchange, Quora, personal blogs, Wikipedia, news articles, and whatever else the crawlers could index. The datasets are enormous, often hundreds of billions of tokens, and the content quality varies wildly.
For engineering topics specifically, this creates a serious problem. Reddit's r/engineering and r/MechanicalEngineering contain useful discussions alongside hobbyist advice, student homework answers, and people confidently stating things that are just wrong.
Forum posts don't come with revision dates or accuracy ratings. A thread from 2011 referencing an outdated standard gets the same weight as current ASME or ISO documentation.
IN PRACTICE
Unlike general AI, Leo uses a Large Mechanical Model trained on 1M+ technical sources - standards, textbooks, datasheets. It also provides citations, so we don't have to guess whether a material property or tolerance is correct.
"Unlike general AI, Leo uses a Large Mechanical Model trained on 1M+ technical sources - standards, textbooks, datasheets. It also provides citations, so we don't have to guess whether a material property or tolerance is correct." - Dorian G., AI Engineer
Where This Goes Wrong in Practice
The failure modes are specific and predictable. Material properties are frequently wrong or imprecise. General AI might give you a typical value for a property without specifying the heat treatment condition, test direction, or applicable standard.
Tolerance and GD&T advice from general AI is particularly unreliable. These are areas where even experienced engineers disagree, and the internet is full of simplified explanations that leave out critical context.
Manufacturing process recommendations are another weak spot. General AI might suggest a process based on what's commonly discussed online rather than what's technically optimal.
The worst part? These errors come wrapped in confident, well-written prose. The model presents incorrect information with the same authoritative tone as correct information.
Why Citations Change Everything
The difference between useful AI and dangerous AI in engineering comes down to one thing: can you verify the answer?
When an AI tool provides a material property, you need to know where that number came from. Was it from the MMPDS handbook? A specific ASTM standard? The manufacturer's datasheet?
Engineers have always worked with references. Good engineering AI should work the same way, giving you an answer and showing you exactly where it came from so you can verify it yourself.
What Purpose-Built Engineering AI Does Differently
Leo AI takes a fundamentally different approach to training data. Instead of scraping the internet, Leo's Large Mechanical Model is trained on over one million vetted engineering sources: industry standards, engineering textbooks, manufacturer datasheets, and peer-reviewed technical literature.
When you ask Leo for a material property, it retrieves the actual value from a verified source and tells you exactly which source it came from. Leo achieves 96% accuracy on technical queries, and every answer comes with traceable citations.
Leo also connects directly to your PDM and PLM systems. It offers integrations with leading platforms including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM.
The Practical Takeaway for Engineering Teams
If you're using general AI for casual research or drafting non-critical text, the training data issue is manageable. Treat it like asking a smart but unreliable colleague.
If you're using AI for anything that touches actual engineering decisions, the training data matters enormously. You need AI that was trained on vetted engineering sources and that provides citations so you can verify every answer.
The engineering profession has always been built on reliable references and traceable decisions. AI tools for engineering should maintain that standard, not lower it.
FAQ
Get Answers You Can Verify
Engineering AI trained on vetted sources, not Reddit.
Try Leo AI and see how cited, accurate answers from 1M+ engineering sources compare to what general AI pulls from the internet.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams
Get Answers You Can Verify
Engineering AI trained on vetted sources, not Reddit.
Try Leo AI and see how cited, accurate answers from 1M+ engineering sources compare to what general AI pulls from the internet.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams