How to Use AI to Write and Audit Welding Documents

Welding inspectors and quality managers are spending more time than ever managing paperwork. Today’s artificial intelligence (AI) tools, such as ChatGPT, Claude, and Gemini, offer a way to change that by enabling near-instant creation, organization, and review of the technical documents CWIs regularly handle.

This article outlines practical ways AI can assist welding inspectors, quality managers, and business owners in reducing their paperwork burden by streamlining the writing and auditing of welding documents. It also explores current limitations, effective prompt-writing techniques, data security considerations, and the future potential of AI-assisted welding quality systems.

AI in Welding Inspection: What It Actually Does

AI is not yet capable of replacing the judgment of a Certified Welding Inspector (CWI), engineer, or quality manager. However, the pace of development — evident in advanced computational engineering efforts such as Leap71’s Noyron model—indicates that the technology is evolving far faster than many anticipated.

At this stage, AI is like a just hired, newly minted CWI, eager to work and full of energy, but still needs direction and supervision to produce high-quality work.

Modern AI systems, commonly known as large language models (LLMs), recognize patterns across enormous amounts of text and generate structured responses based on user instructions.

The first step in working with AI is learning how to give clear instructions.

Writing Better AI Prompts

The quality of AI output depends heavily on the quality of the instructions provided to the system. This process is commonly referred to as prompt engineering.

Avoid vague prompts such as: “Review this welding procedure specification (WPS).”

Instead, provide detailed instructions that define:

1. Who the AI should act as
2. What task it should perform
3. How the response should be   structured
4. Who the intended audience is

For example:

“You are a Certified Welding Inspector with expertise in AWS D1.1, Structural Code—Steel. Review the following WPS for code compliance. Identify nonconformances, missing variables, and items requiring clarification. Always reference the applicable code clauses. Separate findings into ‘Compliant,’ ‘Noncompliant,’ and ‘Needs Clarification.’”

This style of prompting dramatically improves the usefulness of the response.

The key is specificity. AI systems perform best when expectations are clearly defined.

Treat It Like a Conversation

The next thing you need to know is that no matter how well your initial prompt is written, getting what you want out of AI will involve a conversation with multiple clarifications.

Expect to have to review and clarify the AI’s work multiple times. Each time you refine your prompt, you get closer to the desired result.

Don’t be afraid to challenge the AI to defend its work. Treat it like you would treat a conversation with any other CWI. Challenge it to defend every position it takes from the applicable codes or standards.

You can increase the quality of the output by giving the AI access to applicable drawings and codes in your prompt.

Practical Applications for Welding Companies

One of the most valuable uses of AI is accelerating first-pass document reviews.

For example, a fabrication company preparing for a customer audit can use AI to rapidly identify missing variables, inconsistent terminology, qualification gaps, and conflicting information between procedures before the documents ever reach final review by the CWI.

This allows inspectors and quality managers to focus more on higher-level technical decisions rather than repetitive clerical review.

AI can also help smaller fabrication shops that may not have dedicated quality departments by improving documentation consistency and reducing administrative workload.

Understanding the Current Limitations of AI

Despite its strengths, AI has significant limitations that welding professionals must understand.

First, just like many of us, AI systems can confidently provide incorrect information. This issue is commonly referred to as hallucination. The system may generate nonexistent code clauses, inaccurate interpretations, or incorrect technical recommendations. Because of this, AI-generated content should not be accepted without human review.

Second, AI systems do not truly “understand” welding codes in the same way an experienced inspector does. They recognize statistical language patterns rather than applying engineering judgment.

A skilled CWI understands:

• Intent behind code language
• Practical fabrication realities
• Historical interpretations
• Field conditions
• Safety implications
• Customer expectations

AI currently lacks this contextual understanding.

Third, AI is not liable for mistakes. If AI-generated documentation contains errors, responsibility still falls on the company and the qualified personnel who approved the documents.

For these reasons, AI should be viewed as an assistant, not a replacement for qualified welding professionals.

Data Security and Confidentiality

One easy-to-overlook aspect of AI adoption in welding is data security.

Many public AI systems store or process user input externally. This means that welding documentation containing proprietary information may be exposed to the public.

Before uploading documents into any AI system, companies should understand:

• Who owns the uploaded data
• Whether data is retained
• Whether information is used for model training
• How the provider handles encryption and access control

Basic best practices include:

• Removing sensitive customer information
• Avoiding export-controlled data
• Using enterprise-grade AI platforms when possible
• Implementing access controls
• Maintaining document traceability
• Reviewing company cybersecurity policies

Organizations considering large-scale AI integration should involve both quality and IT personnel in deployment planning. Many of the security concerns associated with using external AI can be addressed by building your own in-house system.

Building Internal AI Systems

A great path forward is for each company to build its own in-house AI.

While high-end systems can cost hundreds of thousands of dollars, a small shop can get started with something as simple as a $500 Mac Mini and the download of an open-source AI like Ollama, paired with an agent like OpenClaw.

Developing these systems typically involves:

• Defining the specific problem to solve
• Gathering and cleaning quality data
• Selecting an appropriate AI model
• Testing and validating outputs
• Continuously refining performance

The Future of AI in Welding Inspection

AI tools are improving at an extraordinary pace. What seems impressive today will likely appear primitive in only a few years.

Welding professionals and companies who understand how to use AI will gain significant advantages in productivity and profit.

For now, the most effective approach is a collaborative one where AI handles repetitive, document-heavy tasks, while human experts continue to provide judgment, oversight, and accountability.

Those who begin learning and using these tools now will be better positioned to adapt as the technology evolves.

Conclusion

In its current form, AI is a powerful tool that can help professionals work faster, improve consistency, and reduce administrative burden.

The best way to learn AI is to use it. Go online right now and start talking to any of the many available AI programs so that you can get a feel for how they work and how best to communicate with them.

SETH DAVIS (seth.davis@weldingaudits.com) is a CWI and the owner of Welding Audits LLC., Ordway, Colo.