3 Simple Steps to Minimize AI Hallucinations in Engineering Documentation
- Patrick Law
- Jul 8
- 2 min read
Ever opened an AI-generated spec only to find invented formulas or bogus references? That surprise “Aha!” moment quickly turns into wasted hours of fact-checking—and a whole lot of frustration.
1. Start with Well-Structured Prompts
The root of many hallucinations is an ambiguous instruction. Instead of asking, “Generate a torque conversion table,” supply:
A concise description of the expected format (e.g. JSON with fields for SI value, Imperial value, and units).
One or two concrete examples of correct conversions.
References to your internal style guide or spec sheet.
By converting examples into digestible templates (like JSON or plain text), you remove guesswork. The AI knows exactly what you want and how to deliver it.
2. Link Directly to Exact Documents & Versions
When your prompt points to “the motor spec,” the model may infer outdated or generic info. Instead:
Host your manuals, tables, and charts in a version-controlled repository.
In your prompt, include the precise URL or file name plus version tag—e.g., Motor_Specs_v2.5.pdf.
Ask the AI to cite the source line or paragraph number for every critical value it uses.
This retrieval-augmented generation (RAG) approach keeps the AI grounded in truth and makes it trivial to trace its outputs back to an authoritative source.
3. Embed Human-in-the-Loop Checkpoints
No automated workflow is perfect. Insert lightweight reviews at three key stages:
Outline Review: A subject-matter expert (SME) verifies structure and flags any gaps.
Draft Review: The SME reads the generated content, checking for consistency, correct units, and valid references.
Final Review: A quick sign-off ensures all corrections are applied and no new errors slipped in.
Yes, these reviews add roughly 10–15% more time to your process, and they require a one-time setup of review workflows and roles. But in practice, teams find they save far more time by avoiding post-delivery corrections—plus they gain full confidence in every deliverable.
Real-World Impact at Singularity
At Singularity, we recently needed a full valve-calibration SOP for a high-pressure system. Using these three steps, we:
Crafted a JSON template with sample entries for pressure, temperature, and torque.
Linked to our internal Calibration_Manual_v1.2.pdf, with AI citations of paragraph numbers.
Ran three SME checkpoints, catching two mismatched conversion values before final delivery.
The result? A draft in minutes that required only a 10% review overhead—without a single hallucinated value.
AI can accelerate technical writing—but only if you keep it honest. By combining precise prompts, RAG grounding, and human-in-the-loop reviews, you’ll minimize hallucinations and maximize trust in every document.
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