@article{4562,
  author = {Pit Pichappan},
  title = {Adaptive and Enhanced Retrieval Augmented Generation (RAG) Systems: A Summarised Survey},
  journal = {Journal of Digital Information Management},
  year = {2025},
  volume = {23},
  number = {3},
  doi = {https://doi.org/10.6025/jdim/2025/23/3/190-195},
  url = {https://www.dline.info/fpaper/jdim/v23i3/jdimv23i3_3.pdf},
  abstract = {This brief review examines adaptive and enhanced Retrieval-Augmented Generation (RAG) systems, focusing on overcoming the limitations of standard RAG models, such as inefficiency, excessive resource utilisation,
and rigid retrieve-then-generate workflows. It highlights advancements like Dynamic RAG and Parametric
RAG, which enable context-aware retrieval and parameter-level knowledge integration. The paper emphasises
adaptive mechanisms that intelligently decide when and how much to retrieve, improving efficiency and
relevance. It also discusses query rewriting, verification, and multimodal extensions to enhance precision.
Furthermore, the integration of forecasting latent retrieval methods is introduced, where deep latent dynamics models extract predictable, interpretable components from limited time series data, improving forecasting accuracy. Applications in education, edge computing, and domain-specific contexts are explored,
showing reduced hallucinations and better scalability. The review outlines the evolution of RAG toward
intelligent, responsive systems using reinforcement learning and hybrid architectures. We identified several
potential future research directions, including agentic workflows, multi-modal adaptation, and domainspecific RAG models, which promise more reliable, scalable, and context-aware generative AI applications
across various fields.
},
}