How Manufacturers Preserve Institutional Knowledge With LLMs and RAG as Teams Evolve

3 min read ● Silk Team

Companies face a growing and serious risk: the loss of institutional knowledge. Experienced engineers, operators and technicians have spent years building knowledge; this knowledge has left with them as they retire or leave. Although many companies have documented their processes, there is much of the valuable knowledge that has been developed through email, tribal knowledge, or workarounds that were never documented in a process manual.

To help companies retain institutional knowledge, companies are using a combination of large language models (LLM), along with retrieval-augmented generation (RAG) to enable companies to capture, store, and use institutional knowledge — without requiring a change to how their teams operate today.

Why is Institutional Knowledge Difficult to Capture?

Although companies typically document their processes, the documentation rarely captures all of the critical knowledge. The knowledge is often:

• Contextual (“we do it this way because of what occurred in 2018”)
• Located throughout various forms of documentation including engineering notes, service tickets, and shared drive documentation
• Only known by a limited number of senior employees
• Difficult for new employees to find or understand

Therefore, as employees leave a company, the new employees will repeat mistakes, take longer to ramp up, and become dependent on the few remaining employees who possess this knowledge.

What LLMs & RAG Have Changed

LLMs are good at reading and understanding natural language. However, LLMs require grounding in actual data. RAG provides this grounding by enabling the model to access actual company data.

Practically:

• RAG searches for relevant documentation within company knowledge sources such as standard operating procedures (SOPs), engineering documentation, maintenance logs, incident reports, and historical records
• The LLM uses the retrieved information to generate clear, contextual answers

Employees no longer have to search for documentation; rather, they can ask questions as they would ask a seasoned employee — and receive accurate answers rooted in institutional knowledge.

Transforming Static Documentation into Interactive Assets

Using LLMs and RAG, institutional knowledge is no longer static documentation; rather, it is an active asset that can be interacted with.

Documenting Expertise Before It’s Lost

As employees document their procedures, lessons learned, and postmortems, RAG enables the information to be accessed instantly. Over time, the system will reflect how employees actually perform their jobs — not necessarily how they are intended to perform their jobs.

Enabling New and Transferred Employees

New employees and transferred employees can ask “why” questions, not only “how” questions. This will expedite the onboarding process and also decrease the amount of ad-hoc mentoring required.

Eliminating Repeatedly Asked Questions to Senior Employees

Experienced engineers will no longer spend excessive amounts of time answering the same questions repeatedly. They can now focus on higher value activities, while their knowledge remains accessible to other employees.

Maintaining Continuity When Projects Are Transferred

RAG enables the preservation of history across multiple teams by capturing the historical decisions, constraints, and trade-offs that may have otherwise been lost.

Why This Methodology is More Reliable than Relying on Employee Memory or Generic AI

Companies cannot afford to misinterpret prior decisions. RAG-based systems reduce this risk by:

• Providing a link to the source documentation for the answers generated by the model
• Preventing the model from creating missing detail
• Providing traceability to the original documentation
• Continuing to involve humans in validating and updating the knowledge base

Therefore, the knowledge is both trusted and auditable.

How Manufacturers Can Start

Most organizations will follow a phased approach:

• Identify a key knowledge area (i.e., maintenance procedures or legacy equipment)
• Organize and cleanse existing documentation
• Pilot with a team that is experiencing either high turnover or rapid growth
• Expand as confidence and adoption grow

The goal is continuity, not perfection.

Final Take Away

LLMs and RAG offer manufacturers a means of preserving institutional knowledge, without relying on static documentation or burdening their subject matter expert resources. In addition, LLMs and RAG make experience searchable, explainable, and accessible, therefore protecting the manufacturer from potential risks associated with changes to their workforce.

By utilizing RAG-powered knowledge systems, organizations will ensure that the experience and knowledge of their employees today continue to benefit the organization in the future.