A Survey on Retrieval-Augmented Text Generation for LLMs
This survey organizes the RAG literature into a four-stage framework (pre-retrieval, retrieval, post-retrieval, generation) and traces the paradigm's evolution alongside open challenges.
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Four-pillar framework: The survey breaks each RAG system into pre-retrieval (indexing, query formulation), retrieval (dense/sparse/hybrid), post-retrieval (reranking, compression), and generation (prompt assembly and synthesis).
Evolution of the paradigm: Traces RAG from early dense-retrieval + reader pipelines to modern multi-hop, agentic, and graph-based variants, highlighting how each pillar has been refined.
Evaluation methodology: Covers benchmarks and metrics for retrieval quality, faithfulness, and end-task performance, noting that evaluation remains a bottleneck for comparing systems fairly.
Open directions: Identifies gaps in multimodal RAG, long-context RAG, real-time and streaming RAG, and safe integration with agent workflows.