Overview

• Researchers introduce HANRAG on arXiv, describing a system that routes queries, breaks them into sub-queries, and filters noisy retrieves to address multi-hop question answering.
• The paper reports superior results versus leading methods on single- and multi-hop benchmarks, but the claims have not been peer reviewed.
• Recent explainers outline RAG’s core value: augmenting LLM prompts with retrieved knowledge to reduce hallucinations and keep answers current without retraining the model.
• Coverage reiterates the standard workflow of retrieval, augmentation, and generation, using chunking, embeddings, and vector search to supply context for the LLM.
• Guides emphasize retriever choices and tradeoffs—sparse methods like BM25/TF‑IDF versus dense tools such as LlamaIndex and Haystack—alongside challenges including knowledge-base curation, retrieval quality, latency, and cost.