How is RAG different from training your own model?

RAG doesn't change the model, it adds your documents as context at answer time; cheaper, faster and easier to update.

Does RAG hallucinate?

Less than a bare LLM because answers are grounded with citations, but quality depends on good retrieval and fresh docs.

Cluster

RAG.

2 notes

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.

Notes in this cluster

RAG Without Hallucinations: Chunking, Retrieval and What Drives Answer Quality

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.

July 9, 2026·4 min read·Fryderyk Pryjma

RAG for Technical Documentation: How AI Uses Your Manuals and Service Records

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.

July 9, 2026·4 min read·Fryderyk Pryjma

Related clusters

FAQ

How is RAG different from training your own model?: RAG doesn't change the model, it adds your documents as context at answer time; cheaper, faster and easier to update.
Does RAG hallucinate?: Less than a bare LLM because answers are grounded with citations, but quality depends on good retrieval and fresh docs.