RAG (retrieval-augmented generation) feeds a language model relevant passages retrieved from external sources so its answers rest on current, verifiable data.
Recall in model evaluation is the share of actual positive cases that a model correctly identifies, showing how many relevant items it…
Reinforcement learning is a branch of machine learning in which an agent learns by trial and error to choose actions that maximize…
Reranking is a second retrieval stage that rescores a shortlist of candidate documents with a stronger model so the most relevant ones…
RLHF (reinforcement learning from human feedback) trains a language model to prefer responses that people rate higher, using a learned reward model.