RAG articles

Retrieval-augmented generation grounds a language model in your own documents: retrieve the most relevant passages, place them in the prompt, and ask the model to answer from them. It is the default way to build assistants over company knowledge because it keeps answers current without retraining and lets you show sources.

The hard part is not the pattern but the details. Most wrong answers from a RAG system trace back to ingestion that lost a table, chunks that split an answer in half, or retrieval that returned plausible but wrong passages. Articles in this category focus on those failure points, on how to tell a retrieval problem from a generation problem, and on the architecture decisions that matter once real users and real document sets are involved.

For a structured path through this topic, see Production RAG and LLM Evaluation.