Solution Scenario · RAG · Technical Documentation
Find Technical Knowledge Reliably, with Sources and Valid Versions.
This solution scenario illustrates how product documentation, service knowledge and project experience can be connected in an internal AI knowledge system. RAG combines targeted search across approved sources with a language model.
PDFs, Wikis, Projects
Roles and approvals
+
Sprachmodell
Verifiable and testable
Use in process
Illustrative Starting Point
The Right Answer Exists, Spread across Documentation, Projects and People.
A technical company supports highly configurable products. Manuals are stored in the DMS, supplementary notes in a wiki, solved special cases in project folders and important experience knowledge with individual specialists. Sales and service therefore ask the same questions repeatedly and cannot always identify which information is still valid.
The target state is not a general-purpose chatbot but a controlled research process: the question is classified, only approved sources are searched and the answer shows source locations, document versions and possible uncertainty. The pilot begins only after currency, permissions and ownership have been clarified.
Before the RAG pilot
The knowledge landscape must first become decidable.
Data & AI readiness captures the relevant channels, evaluates the sources, and determines what processing and governance are necessary for reliable AI access.
Make documents, systems, interfaces, and informal knowledge pathways visible.
Clarify quality, timeliness, redundancies, rights, and responsibilities.
Define structure, metadata, maintenance, and a realistic pilot scope.
When
Versions Are Difficult to Identify
Manuals, technical notes and project documents exist in multiple versions and repositories.
When
Answers Must Be Supported by Evidence
Sales and service need sources, validity information and clear limits rather than merely plausible wording.
When
Specialists Repeatedly Resolve the Same Questions
Repeated research and follow-up questions consume specialist time even though the required knowledge already exists.
System logic
A RAG system is built on a curated knowledge base.
Quality arises from the interplay of data preparation, permissions, chunking, retrieval, prompting, response logic, and monitoring. The language model is just one component in this chain.
Structure · Metadata · Versions · Responsibilities
Permissions · Quality · Versions · Limits
Targeted Search · Language Model · Prompts · Source References
Answers · Summaries · Exams · Actions
RAG Pilot
A good pilot answers the right questions first.
After the readiness phase, the pilot tests a clearly defined knowledge domain. The goal is reliable evidence of which sources work, which questions matter and where measurable workload reduction is possible.
01
Define Questions and Users
Which product or service questions should be answered, for whom and with what business value?
02
Review Sources and Versions
Assess documentation, currency, permissions, metadata and responsible owners realistically.
03
Test the Research Process
Build a focused RAG workflow with representative test questions, source display and clear limits.
04
Decide on Quality and Operations
Evaluate answers with subject-matter experts and derive the roadmap, integrations, maintenance process and next expansion step.
Use Cases
Potential Application Areas for RAG.
Why Trixner?
RAG requires technical depth and process understanding.
The crucial difference lies not in the chosen language model, but in how knowledge is prepared, found, limited, and embedded into existing work.
Trixner Digital Solutions provides support from the initial potential analysis through to a robust RAG pilot and subsequent integration into internal systems.
Further Reading: Why RAG Projects Begin with Data helps assess the starting point; How to Plan a RAG Pilot defines the scope, test questions and success criteria. The service RAG Pilot.
Next Step
Let us assess whether this scenario fits your situation.
In the discovery call, we assess data channels, the current state and maturity level. This shows whether data preparation, readiness support or a RAG pilot is the right next step.