📄️ Re2 (re-reading the question)
Prompt engineering is full of small tricks that look almost too simple to work. Re2 ("re-reading") is one of them. The paper Re-Reading Improves Reasoning in Large Language Models (Xu et al., 2023) shows that if you present the user's question twice, with a short instruction between the two copies, instruct models often answer more carefully. The model effectively gets a second look at the question inside a single user message — without an extra LLM call.
📄️ Prompt injection — how documents enter the LLM
After optional query-time machinery has run — summarization, fetch check, HyDE, retrieval, rerank, rewrite, Re2 — RAG Me Up still has to do what users think of as "the AI": build a prompt and call the LLM. This page is about how evidence is injected, with concrete examples of the resulting message thread.
📄️ LLM backends
Every generative step in RAG Me Up — answering, HyDE drafts, fetch-check decisions, rewrite judgements, summarization, LLM provenance — goes through LLMHelper. Understanding that class is understanding how provider choice, history handling, and streaming interact with the rest of the pipeline.