Supporting Research

RAG & Knowledge Systems

5 Articles

Enterprise AI must be rooted in organizational knowledge, policies, procedures, domain expertise, and historical context. This area encompasses retrieval-augmented generation (RAG) architectures, knowledge graph construction, vector databases and semantic search, context management strategies, hybrid retrieval approaches, and knowledge system evaluation.

It’s important to understand when RAG is adequate and when a Trust Layer architecture is necessary. Additionally, it’s essential to know how to design for retrieval at scale, implement practical patterns for building knowledge bases, and develop integration strategies for Enterprise Digital Twins. These elements form a critical foundation for reasoning systems that require access to up-to-date organizational knowledge without needing to retrain the models.

Who This Is For

CIOs, VPs of AI, Enterprise Architects, AI Researchers, Technical Decision-Makers

Key Topics

  • Multi-Agent Reinforcement Learning for RAG optimization
  • Retrieval Interleaved Generation with Google's DataGemma
  • LongRAG for large-scale enterprise knowledge systems
  • Advanced retrieval architectures beyond traditional RAG

ReaRAG: A Knowledge-Guided Reasoning Model That Improves Factuality in Multi-hop Question Answering

March 30, 2025 16 min read

The ReaRAG factuality reasoning model introduces a breakthrough in retrieval-augmented generation by combining structured reasoning with external knowledge retrieval. Built around a Thought → Action → Observation (TAO) loop, ReaRAG enables large reasoning models to reflect, retrieve, and refine their answers iteratively — significantly improving factual accuracy in multi-hop question answering (QA) tasks. Unlike prompt-based RAG systems like Search-o1, ReaRAG avoids overthinking and error propagation by dynamically choosing when to retrieve or stop reasoning. This article explores ReaRAG’s architecture, training pipeline, benchmark performance, and strategic importance in the shift from generation to retrieval-augmented reasoning. Whether you’re an AI researcher, engineer, or enterprise leader, this is your comprehensive guide to the future of explainable, knowledge-guided AI systems.

Read Article →

Optimizing Retrieval-Augmented Generation (RAG) with Multi-Agent Reinforcement Learning (MMOA-RAG) and MAPPO

February 2, 2025 11 min read

Retrieval-Augmented Generation (RAG) enhances AI by incorporating external knowledge, but optimizing its modules independently leads to inefficiencies. MMOA-RAG (Multi-Module Optimization Algorithm for RAG) solves this by using Multi-Agent Reinforcement Learning (MARL) and MAPPO (Multi-Agent Proximal Policy Optimization) to train RAG components—query rewriting, document retrieval, and answer generation—collaboratively.

This approach improves response accuracy, document selection quality, and overall system efficiency through gradient synchronization, parameter sharing, and reinforcement learning-driven penalty mechanisms. By aligning the objectives of multiple agents, MMOA-RAG reduces hallucinations, increases factual consistency, and ensures retrieval relevance.

Benchmark evaluations show MMOA-RAG surpasses traditional RAG methods, demonstrating higher accuracy and stability across various datasets. Whether you’re an AI researcher, developer, or industry professional, this article provides an in-depth look at how multi-agent learning is transforming AI-driven retrieval systems.

Read Article →

RARE: Retrieval-Augmented Reasoning Enhancement for Accurate AI in High-Stakes Question Answering

December 5, 2024 16 min read

Artificial Intelligence (AI) has transformed how we interact with information, with Question Answering (QA) systems powered by Large Language Models (LLMs) becoming integral to decision-making across industries. However, challenges like hallucinations, omissions, and inconsistent reasoning hinder their reliability, especially in high-stakes domains like healthcare, legal analysis, and finance.

This article explores RARE (Retrieval-Augmented Reasoning Enhancement), an innovative framework designed to address these limitations. By integrating retrieval-augmented generation with a robust factuality scoring mechanism, RARE ensures that answers are accurate, contextually relevant, and validated by trusted external sources. Key features like A6: Search Query Generation and A7: Sub-question Retrieval and Re-answering enhance LLMs’ ability to reason logically and retrieve domain-specific knowledge.

RARE’s performance, validated across benchmarks like MedQA and CommonsenseQA, demonstrates its ability to outperform state-of-the-art models like GPT-4, proving its scalability and adaptability. Its applications extend to medical QA, where it mitigates risks by grounding reasoning in up-to-date evidence, safeguarding patient outcomes.

This article dives into RARE’s architecture, performance, and future potential, offering insights into how this cutting-edge framework sets a new standard for trustworthy AI reasoning systems. Discover how RARE is reshaping the landscape of AI-driven question answering.

Read Article →

Enhancing AI Accuracy: From Retrieval Augmented Generation (RAG) to Retrieval Interleaved Generation (RIG) with Google’s DataGemma

September 13, 2024 10 min read

Artificial Intelligence has advanced significantly with the development of large language models (LLMs) like GPT-4 and Google’s Gemini. While these models excel at generating coherent and contextually relevant text, they often struggle with factual accuracy, sometimes producing “hallucinations”—plausible but incorrect information. Retrieval Augmented Generation (RAG) addresses this by retrieving relevant documents before generating responses, but it has limitations such as static retrieval and inefficiency with complex queries.

Retrieval Interleaved Generation (RIG) is a novel technique implemented by Google’s DataGemma that interleaves retrieval and generation steps.
This allows the AI model to dynamically access and incorporate real-time information from external sources during the response generation process. RIG addresses RAG’s limitations by enabling dynamic retrieval, ensuring contextual alignment, and enhancing accuracy.

DataGemma leverages Data Commons, an open knowledge repository combining data from authoritative sources like the U.S. Census Bureau and World Bank. By grounding responses in verified data from Data Commons, DataGemma significantly reduces hallucinations and improves factual accuracy.

The integration of RIG and data grounding leads to several advantages, including enhanced accuracy, comprehensive responses, contextual relevance, and adaptability across various topics. However, challenges such as increased computational load, dependency on data sources, complex implementation, and privacy concerns remain.
Overall, RIG and tools like DataGemma and Data Commons represent significant advancements in AI, paving the way for more accurate, trustworthy, and effective AI technologies across various sectors.

Read Article →

LongRAG vs RAG: How AI is Revolutionizing Knowledge Retrieval and Generation 

June 29, 2024 15 min read

LongRAG, short for Long Retrieval-Augmented Generation, is revolutionizing how AI systems process and retrieve information. Unlike traditional Retrieval-Augmented Generation (RAG) models, LongRAG leverages long-context language models to improve performance in complex information tasks dramatically. By using entire documents or groups of related documents as retrieval units, LongRAG addresses the limitations of short-passage retrieval, offering enhanced context preservation and more accurate responses.

This innovative approach significantly reduces corpus size, with the Wikipedia dataset shrinking from 22 million passages to just 600,000 document units. LongRAG’s performance is truly impressive, achieving a remarkable 71% answer recall@1 on the Natural Questions dataset, compared to 52% for traditional systems. Its ability to handle multi-hop questions and complex queries sets it apart in the field of AI-powered information retrieval and generation.

LongRAG’s potential applications span various domains, including advanced search engines, intelligent tutoring systems, and automated research assistants. As AI and natural language processing continue to evolve, LongRAG paves the way for more efficient, context-aware AI systems capable of understanding and generating human-like responses to complex information needs.

Read Article →