Industrial RAG: AI over Technical Documentation
Critical technical documentation exists. The problem is that no one can query it when it matters. This course teaches you to build a local RAG system that converts manuals, RCAs, and procedures into operational answers — without exposing data to the cloud.
Technical documentation exists.
The problem is that no one can query it when it matters.
On the floor, every shift repeats questions that have already been answered. The manual is buried in a 400-page PDF, the RCA is in a different folder, last month's shift log is on a server that someone has to track down. Critical technical knowledge exists — but it is scattered, disorganized, and inaccessible at the moment it counts. That costs hours, slow decisions, and the repetition of already-diagnosed failures.
The challenge is not "having AI." It is converting dead documentation into an operational response layer: a system that the shift technician can query in natural language and that answers from the plant's real manuals. Without sending data to the cloud. Without depending on an external model that has no context of your operation. Without exposing sensitive information to infrastructure outside your control.
RAG — Retrieval Augmented Generation — is the architecture that solves exactly that problem. But most RAG courses are designed for office demos, not for industrial environments with OT/IT constraints, heterogeneous technical documentation, and zero tolerance for error. This course is designed for the real industry: equipment manuals, safety procedures, failure analyses, and shift logs under security and operational continuity requirements.
What you will build
Four modules that take you from the foundations of applied RAG to deploying a functional system over real technical documentation.
What RAG solves — and what it does not. Concrete industrial cases: equipment manuals, RCAs, operating procedures, shift reports. The difference between a generic document chatbot and a system useful for operations under real constraints. When RAG is the right answer and when it is not.
3 hoursOn-premise or edge design with no cloud dependency. Local embedding models, vector storage without external APIs, role-based access control. Considerations for OT/IT-constrained environments: network segmentation, sensitive data, and operational continuity. Keeping data where it belongs.
4 hoursTechnical chunking: how to split long manuals without losing context. Metadata and document versioning. Retrieval strategies for technical language, plant-specific acronyms, and specialized terminology. Grounding evaluation: how to measure whether the system is answering from the correct source. Reducing hallucinations in critical environments.
5 hoursFull document ingestion pipeline. Connection with a query interface for plant technicians. Test cases with real operational and maintenance questions. Response evaluation. Roadmap for moving from PoC to operational asset: how to scale without losing source control and without breaking the document update process.
4 hoursWhat you will be able to do
Complete on-premise architecture for querying technical documentation in natural language — no external APIs, no public cloud, no exposure of sensitive plant data.
Evaluate grounding, reduce hallucinations in technical documentation, and maintain role-based access control. Build a system that answers from the correct source — not from what the model "knows."
A concrete roadmap for moving from a working prototype to a stable operational asset, with document update criteria, response traceability, and basic technical governance.
Who teaches this course
Seven years leading data ecosystems at BCI, work at Canadian ad-tech firm illumin, with stints at the IDB, PDVSA, Falabella, and Walmart across five countries. Founded Yaripo to close the gap between AI strategy and real implementation for mid-market organizations with critical operations.
Yaripo is the only consultancy that designs RAG systems specifically for industrial environments with OT/IT constraints, heterogeneous technical documentation, and local, cloud-free operation requirements — built for organizations in regulated markets. While most training offerings teach RAG for generic digital applications, Yaripo grounds it in real plant conditions: equipment manuals, safety procedures, shift logs, and failure analyses under connectivity and operational continuity constraints, with privacy-by-design at every layer.
What engineers ask before enrolling
Your technical documentation
can answer in seconds.
16 hours. 4 modules. A functional RAG system over your own documentation, with no cloud and no exposure of sensitive plant data.
Enrollment at academia.yaripo.cl · Online asynchronous · SENCE-compatible