This cluster explains how to build RAG on technical documentation (manuals, machine instructions, service cards) so answers are accurate and grounded in sources. We cover what drives quality (chunking, retrieval, document freshness), where RAG fails and how to constrain it, and how to combine it with a service assistant.
An AI assistant's answer quality depends more on the state of your documentation than on the model. Six steps that turn DTR, manuals and standards into a base the assistant actually uses.
A machine's DTR is the full pack: build, operation, maintenance, safety. What it really contains, how to organise it, and where AI genuinely helps, and where it does not.
Why RAG sometimes gets it wrong and how to limit it. Chunking, embeddings, reranking, designing the "I don't know" answer, and how to measure quality before you trust the answers.
How to make AI answer from your own documentation (manuals, technical sheets, service records) instead of from memory. What RAG is, why it fits technical documentation, and where its limits are.
