How RAG and LLMs Help Manufacturing Teams Eliminate Engineering Bottlenecks

3 min read ● Silk Team

The bottleneck that engineering teams create in manufacturing operations is largely caused by how much time is spent on answering repetitive questions. Engineers spend too much time answering repetitive questions about product specs, legacy designs, trouble shooting procedures and regulatory requirements that are readily available, however have been locked away in technical documentation, prior projects, and institutional knowledge.

Large Language Models (LLMs) in conjunction with Retrieval-Augmented Generation (RAG) can make a real difference in reducing engineering dependency, increasing the speed, accuracy and collaboration among teams in manufacturing organizations.

Why Do Engineering Bottlenecks Exist?

Engineers are often the default “go-to” people for answers to other departments and teams within manufacturing organizations. These teams include:

• Operations – “What’s the correct spec for this component?”
• Maintenance – “Has this type of problem occurred before?”
• Sales – “Which design version should I be using?”

The problem isn’t the lack of documentation, it’s the availability of the documentation. Product documentation is often dispersed through various formats including: CAD notes, PDF documents, shared drive documents, ticketing systems and e-mail exchanges. In addition, finding the correct answer may require interrupting engineers that are already overwhelmed with their current workload.

What Can RAG & LLM Do Together?

While LLMs are excellent at generating and understanding natural language, they do not understand the engineering history of your company. RAG is the missing piece in this process because it allows you to use the engineering data stored internally to provide context for the generated answers.

In Practice:

• RAG finds the relevant content from engineering documents, SOPs, drawings, change logs and past incident reports.
• LLM uses the content found by RAG to generate a clear, contextual answer based only on the data retrieved.

The result is an internal engineering knowledge base assistant that provides accurate answers without requiring an engineer to stop work each time a question needs to be answered.

Where Manufacturers Will See the Greatest Impact

Faster Troubleshooting on the Shop Floor

Maintenance and operations teams will be able to quickly find answers to fault code questions, tolerance questions and procedure questions and get immediate guidance backed by approved documentation. This results in less downtime and fewer escalations.

Reduced Interruption Time for Engineering Teams

No longer will engineers need to answer the same questions over and over again; instead, they can concentrate on designing and optimizing products and solutions. The system will handle all routine knowledge requests automatically.

Improved Knowledge Sharing Between Departments

By using RAG powered tools, manufacturing organizations will be able to break down departmental barriers by providing engineering knowledge to non-engineering teams and ensure that this knowledge is provided accurately and securely.

Better Use of Past Project Failures, Design Decisions

Many past projects, failures and design decisions contain valuable knowledge. However, this knowledge is often locked away due to the departure of the original engineers. RAG makes this institutional knowledge easily searchable and usable.

Why is This Approach Safer than Using a Generic AI Tool?

Manufacturing companies cannot afford to use generic AI tools that provide vague or inaccurate answers. RAG-based systems greatly reduce the risk associated with using AI tools by:

• Restricting answers to approved internal documents.
• Ensuring that answers are always current and revised.
• Providing a paper trail back to the source document.

This makes it easier for manufacturing companies to implement RAG based systems in a regulated environment, especially those that are safety critical.

Getting Started without Disrupting Daily Operations

Most successful implementations start with a small scope:

• Start with a single area of the engineering function (i.e., equipment maintenance or design standards).
• Clean and organize the documentation before indexing.
• Pilot test with operations or maintenance teams.
• Measure the amount of time it takes to respond and the number of times the engineering team has to escalate issues.

The goal of implementing a RAG-based system is to eliminate friction and not replace the engineering team.

Summary

The use of RAG and LLM to reduce engineering bottlenecks is less about automation and more about accessibility. Manufacturing organizations can free up their most valuable technical resource and improve operational efficiency by using RAG and LLM to turn existing engineering knowledge into instant and reliable answers.

For manufacturing organizations that want to grow and expand their operations without exhausting their engineering resources, RAG-based knowledge systems are emerging as a viable and highly impactful solution.