Retrieval-Augmented Generation & Knowledge-Grounded AI

RAG Systems

Combining vector search, knowledge graphs, and LLMs for factual, grounded, up-to-date AI responses.

Overview

Retrieval-Augmented Generation (RAG) addresses one of the fundamental limitations of LLMs — static knowledge cutoffs and hallucination. By coupling LLMs with retrieval systems that can fetch relevant, up-to-date information at inference time, RAG enables AI systems that are both generative and factually grounded.

Mentneo's RAG research covers advanced indexing strategies, multi-step retrieval pipelines, cross-lingual retrieval, hybrid sparse-dense retrieval, and RAG evaluation frameworks.

Key Research Topics

  • Dense passage retrieval
  • Hybrid sparse-dense retrieval
  • Multi-step RAG pipelines
  • Knowledge graph augmentation
  • RAG evaluation (faithfulness, relevance)
  • Long-context RAG
  • Streaming RAG
  • Domain-specific RAG

Products Using This Research

Mentneo Search (RAG-powered enterprise search)Mentneo Docs AICustomer support automationResearch assistants

RAG Systems — Common Questions

RAG is an AI architecture that combines a large language model with a retrieval system. When a query is received, the system first retrieves relevant documents or passages from a knowledge base, then provides them as context to the LLM, which generates a grounded response. This reduces hallucination and keeps responses factually accurate and up-to-date.

RAG is ideal when your knowledge base changes frequently, when you need source citations, or when you have large amounts of proprietary data you cannot include in training. Fine-tuning is better for learning specific styles, formats, or behaviors. Many production systems use both: fine-tuning for behavior and RAG for knowledge.

Mentneo uses a multi-stage RAG pipeline: (1) document chunking with semantic boundaries, (2) hybrid sparse-dense indexing using both keyword (BM25) and embedding-based retrieval, (3) cross-encoder reranking, (4) context compression, and (5) grounded generation with citation tracking. This stack powers Mentneo Search and Docs AI.

Related Research Areas

EmbeddingsKnowledge GraphsLarge Language ModelsAi Infrastructure