What Is RAG — And Why Engineers Should Care

Have you ever used ChatGPT and thought, “That sounded smart… but is it actually right?” That’s because most AI tools are trained on general data and don’t know your specific project files, SOPs, or standards. They guess. And in engineering, guessing isn’t good enough.

That’s why we’ve started using something better: RAG, or Retrieval-Augmented Generation.

So, What Is RAG?

RAG is a way to upgrade your AI. It lets ChatGPT look through your own documents before answering. Instead of relying only on what it memorized during training, it searches the latest specs, manuals, and templates you’ve uploaded — then builds an answer using those files.

Think of it like giving your AI a shortcut to your company’s knowledge base.

How Does It Work?

RAG has two key parts:

1. Retriever – This part searches your files (like client specs or safety standards) and picks out the ones that are most relevant to your question.

2. Generator – This part reads those documents and uses them to generate an accurate, grounded answer.

The result? No more hallucinations. Just answers you can trace back to real sources.

Why We’re Using It at Singularity

We’ve just started rolling out RAG in our engineering workflows. It’s already making a difference. Instead of wondering if ChatGPT got something wrong, we can now check where it pulled its answer from — and confirm it came from our real project files.

From design reviews to spec checks, RAG helps our team move faster and trust the results.

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