Why Your AI Keeps Giving You Confident, Wrong Answers (And How to Fix It)

You're working on a harness assembly. You need to know the mating connector for part ABC-123 and the correct crimp tool for 18 AWG wire. Simple technical question.

You ask your AI assistant. It responds immediately, confidently:

"Use connector XYZ-456 with crimp tool T-180."

Sounds plausible. The part numbers look right. The model seems certain.

But is it correct?

Without being an expert in that specific connector family, you have no way to know. And if the AI is wrong—which happens more often than you'd think—you won't find out until you're on the production floor with the wrong parts.

This is the hallucination problem that keeps manufacturers from trusting AI for technical questions. The model sounds confident even when it's making things up. And the higher the stakes, the less acceptable that becomes.

The Engineer Bottleneck

Before AI, the workflow for technical questions was straightforward: ask an engineer.

Sales needs to know if a connector is rated for high-temperature environments? Email engineering. Production has a question about wire gauge compatibility? Wait for the engineer to respond. Purchasing needs to verify an alternate part? Get in line.

For a small team of engineers supporting a larger organization—sales, production, purchasing, project management—this creates a bottleneck. Not because engineers are slow, but because there aren't enough of them to answer every technical question in real time.

The promise of AI was supposed to fix this: democratize access to technical knowledge, give everyone instant answers, eliminate the bottleneck.

The reality: AI gives you instant answers, but you can't trust them without verification. So you still ask the engineer.

The bottleneck remains. Now you just have an extra step.

Why "Just Upload Everything" Doesn't Work

The obvious solution seems simple: give the AI access to all your technical documents. Upload all your datasheets, specs, drawings, and purchase orders. Let the model search through everything and find the answer.

This is what most organizations try first. And it doesn't work.

Here's why: imagine you're working on a specific work order. There are 10 files associated with it—engineering drawings, purchase orders, specifications, assembly instructions, material certs. Those 10 files might contain 150,000 words combined.

When you ask "What's the delivery date for this order?", you're asking the model to find one specific piece of information buried somewhere in those 150,000 words. The technical term is "needle in a haystack."

The model has two problems:

1. Noise - With everything loaded, it often picks information from the wrong document or misses the right answer entirely because there's too much data to sort through.
2. No traceability - When it does give an answer, you can't see where it came from. Did it pull that from the actual specification, or did it hallucinate based on similar-sounding information from a different project?

You end up checking everything manually anyway, which defeats the purpose.

The Better Approach: Give the Model a Search Engine

Instead of dumping all possible files into the system and hoping it finds the right information, we built something different: we give the model the ability to fetch files on demand, the same way you would.

Think of it like this: When you have a technical question, you don't memorize every datasheet in your filing cabinet. You go find the specific datasheet you need, open it, and look up the answer.

That's what we let the model do.

You ask: "What's the mating connector for part ABC-123?"

The system:

1. Identifies which work order you're talking about
2. Finds the relevant files (BOM, datasheet for ABC-123)
3. Searches those specific files for the answer
4. Returns the answer with a citation showing exactly which file and page it came from

You can click through and verify in seconds. The datasheet is right there, highlighted to the relevant section.

This solves both problems:

• Less noise - The model only searches the files that matter, not everything ever written about connectors
• Full traceability - Every answer includes the source, so you can verify without being an expert

The Datasheet Finder: When You Don't Have the File

Here's where it gets more powerful.

Often, the customer doesn't have the file on hand. You're working with a purchased component—a connector, a switch, a specialized cable assembly—and you need to know the specifications. Voltage rating, temperature range, torque specs, mating connector compatibility.

You could rely on the model's training data. If it's a common part, the model probably saw the datasheet during training and might know the answer.

But "might" isn't good enough in manufacturing.

So we built a tool we call the datasheet finder. It does exactly what it sounds like: when the model needs a datasheet it doesn't have, it searches the internet, finds the official manufacturer datasheet, and retrieves it.

Then it shows you what it found.

The workflow looks like this:

You: "What's the mating connector for ABC-123 at 18 AWG?"

System: [Searching for datasheet: ABC-123]
Found: Molex_ABC-123_Series_Datasheet.pdf
[Loading relevant sections]

Answer: Based on the compatibility table (page 4), use connector ABC-123-J3 for the mating end. For 18 AWG wire, the crimp tool is X-220 with die D-18 per the tooling guide (page 12).

Sources: Molex_ABC-123_Datasheet.pdf (pp. 4-5) | Crimp_Tools_Guide_RevD.pdf (p. 12)

[View datasheet] [Verify specification]

This is human-in-the-loop by design. The model does the work—finds the datasheet, extracts the answer—but you get to verify. Click through to the actual datasheet, check that it's the right part, confirm the answer makes sense.

You're not trusting the AI blindly. You're using it to do the research, then verifying with the source documents.

What This Changes Organizationally

Remember the engineer bottleneck?

This doesn't eliminate the need for engineering expertise. But it changes the workflow fundamentally.

Before:

Production: "What mating connector do we need for ABC-123?"
[Wait 2 hours for engineer to respond]
Engineer: "Use ABC-123-J3"
Production: "Thanks" [proceeds with work]

After:

Production: "What mating connector do we need for ABC-123?"
System: "Use ABC-123-J3 per Molex datasheet page 4"
[Click to verify datasheet]
Production to Engineer: "Planning to use ABC-123-J3 based on the attached datasheet. Moving forward unless you see an issue."

The email changes from "waiting for an answer" to "confirming before proceeding." The engineer's time shifts from answering routine questions to catching edge cases and handling complex judgments.

This democratizes access to technical information across the organization. Sales can answer basic technical questions during customer calls. Production can verify specifications without waiting. Purchasing can check alternate parts before placing orders.

People learn as they go. The model is excellent at explaining technical concepts, and because every answer includes the source document, people can dig deeper when they want to understand why something is specified a certain way.

The Hallucination Prevention Strategy

This approach significantly reduces hallucinations, but not by making the model smarter. By changing how it works:

1. Grounding in real documents
The model doesn't generate answers from its training data. It searches specific files and cites what it finds.

2. Abstaining when uncertain
If the datasheet is ambiguous, or if there's conflicting information across revisions, the system flags it: "Needs confirmation—two versions found." You decide which one applies.

3. Showing its work
Every answer includes the source files and page numbers. You can verify in seconds, even if you're not an expert in that specific component.

4. Human verification built in
We don't hide the sources behind the answer. They're front and center, one click away. The workflow assumes you'll check.

This ties directly back to the four stages of AI adoption we've written about before. This is Stage Two—intelligently exposing your data so the model can actually function as a useful team member. Not dumping everything into context and hoping it works, but building infrastructure that lets the model access the right information at the right time.

What This Looks Like in Practice

A typical morning for a project manager or production lead:

They're reviewing work orders, preparing for the day's builds. A question comes up about a connector specification. Instead of:

• Digging through file folders to find the right datasheet
• Emailing engineering and waiting
• Guessing based on similar parts they've used before

They ask the system. It fetches the datasheet, provides the answer with a citation, and they verify it in 30 seconds. They move on.

Throughout the day, dozens of these small technical questions come up. Each one used to require either waiting for an engineer or making an educated guess. Now they're resolved immediately, with verification built in.

The cumulative effect: faster decisions, fewer errors, less engineering time spent on routine questions.

Who This Matters Most For

Manufacturing Engineers & Production Leads
Need fast answers on connector compatibility, crimp tools, wire gauges, torque specs—before starting a build. Wrong answers mean scrap or rework.

Purchasing & Supply Chain
Verifying alternates, checking specifications, confirming lead times without waiting for engineering approval on every decision.

Project Managers
Getting quick answers they can verify and forward to customers or internal teams without becoming a bottleneck themselves.

Quality & Compliance
Need traceability on every specification decision. The cited sources create an automatic audit trail.

What You Need to Build This

The technical requirements are straightforward:

1. Read-only access to your document systems - Where you store datasheets, specs, drawings, purchase orders
2. ERP/PLM integration - To understand which files are relevant to which work orders
3. Internet access for public datasheets - When you don't have the manufacturer's latest datasheet on file
4. Citation framework - Every answer includes the source file and page number

The harder part is the organizational shift: training people to verify answers using the provided sources rather than either blindly trusting the AI or ignoring it entirely.

That's where we come in. We build the tools and provide the embedded training that helps teams actually adopt this workflow.

The Bottom Line

The hallucination problem isn't going away by making models smarter. It's solved by changing how they work:

• Search specific files, not everything
• Cite every answer
• Abstain when uncertain
• Make verification fast and easy

This turns AI from "sounds confident but might be wrong" into "here's what I found, check page 4 if you want to verify."

That's the difference between a tool that creates more work (because you have to fact-check everything) and a tool that actually eliminates bottlenecks.

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