How Manufacturers Use RAG Models to Scale Technical Content Without Sacrificing Accuracy
3 min read ● Silk Team
There is so much technical content created by manufacturers — product manuals, installation guides, maintenance procedures, safety documentation, training materials, and compliance updates. Keeping these forms of technical content current and correct is becoming increasingly difficult due to evolving regulations and expanding product lines.
Most organizations want to utilize AI to create technical content faster; however, there is a good reason many do not. Technical content needs to be precise; errors in technical content can lead to product downtime, safety risks, and compliance violations. This is where Retrieval-Augmented Generation (RAG) models present a viable path toward this objective.
While RAG does not replace the technical expertise needed to develop technical content, RAG enables manufacturers to increase their ability to create technical content quickly, without sacrificing control and accuracy.
Challenges to Scaling Technical Documentation
There are several aspects to technical documentation that make scaling challenging:
- Content requires high accuracy; there is no room for approximation
- Documentation may contain information found in multiple types of documentation: engineering documentation, SOPs, CAD documentation, and service records
- Changes to documentation require collaboration among teams
- Subject Matter Experts (SMEs) become the bottleneck in developing documentation
Therefore, documentation is typically developed after the fact, and/or engineers will need to take time away from development and improvement of their products to rewrite the content.
What RAG Brings to Technical Content Development
RAG provides a new paradigm in which AI generated content is tied to internal approved sources.
RAG operates using two primary functions:
- Retrieval: The system extracts applicable information from existing documentation, engineering standards, product specifications, and previous content history
- Generation: The language model creates new or updated content based solely on what was retrieved
By linking generated content to the previously validated internal sources, RAG ensures generated content accurately reflects how products function — not general assumptions about those products.
Examples Where Manufacturers Are Using RAG to Scale Technical Content
Faster First Drafts of Manuals & Guides
Technical writers can now use RAG to create initial drafts of user manuals, work instructions, or setup guides that are tied to the actual product data, and prior documentation.
Consistent Changes to Content Across Product Variants
When changes occur to product specifications or when new product variants are released, RAG can help to propagate those changes across related documentation reducing inconsistencies and manual rework.
Reusing Content Across Audiences
Documentation can be reused across various audience groups (technicians, operators, customer), while remaining consistent with valid technical facts.
Assistance Supporting Global and Multilingual Teams
RAG models can also assist with creating localized version of technical documentation by accessing the same authoritative source material to improve consistency across regions.
Why RAG is Safer Than Generic AI Writing Tools
Manufacturers cannot afford to allow AI to create content that has never been seen before. RAG minimizes the risk of:
- Restricting the output to validated internal documentation
- Preventing hallucination if the information is missing
- Providing a record of origin of the source content
- Ensuring that humans remain in the approval process
These capabilities make RAG acceptable in regulated, safety critical environments where trust is paramount.
How Teams Work with RAG in Real-World Applications
RAG rarely replaces technical writers or engineers. Instead, it assists them.
The typical workflow for a team using RAG would look like the following:
- A writer or engineer identifies the purpose of the content to be created
- RAG finds all relevant internal documentation
- The model produces a structured draft of content
- The team reviews, refines, and approves
This process allows teams to dramatically reduce the time required to write repetitive documentation and maintain accountability.
Initial Steps to Getting Started with RAG
Successful manufacturers who have used RAG in production have taken the following approach to get started:
- Focused on one form of documentation (e.g., maintenance procedures)
- Organized and structured existing documentation
- Defined and documented review and approval processes
- Measured time savings and reductions in errors
Scaling occurs naturally when the trust level within the organization is established.
Conclusion
RAG models empower manufacturers to grow the production of technical content without compromising the quality and integrity of that content. By having the AI generated content drafts connected to the same trusted knowledge base as the original documentation, teams are able to build content faster, eliminate bottlenecks and keep the documentation aligned with reality.
For manufacturers who are trying to achieve balance between speed, accuracy and compliance, RAG is not a shortcut to generating technical content quickly; rather, it represents a long-term method to generate technical content at scale.