RAG
RAG on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on rag in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around rag, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Optimizing Retrieval-Augmented Generation (RAG) systems hinges on precise question parsing. Loop Engineering for RAG, detailed in our latest Enterprise Document Intelligence report [Vol.1 #6quinquies], introduces a streamlined approach: a deliberately small loop focused on question refinement. This involves reading the document, identifying gaps, and re-parsing the query—a critical step before retrieval. Explore this technique to enhance accuracy and efficiency. For a foundational understanding of iterative learning processes, consider “Backpropagation Explained for Beginners (Part 1).”
EU AI Act OpenRAG: 933 legally structured chunks and BGE-M3 embeddings in one SQLite file [P]
Introducing EU AI Act OpenRAG, a meticulously structured resource for legal-NLP experimentation. This downloadable corpus, based on Regulation (EU) 2024/1689, comprises 933 legally-aligned chunks—organized by article paragraph, recital, and definition—within a single SQLite file. Utilizing BGE-M3 embeddings, it delivers a normalized 1024-dimensional vector for each chunk, alongside EUR-Lex links and application-date metadata. Initial evaluations demonstrate improved recall and QA performance compared to baselines, showcasing the value of structural chunking. Explore the dataset at huggingface.co/datasets/faitholopade
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
Enterprise AI organizations face a critical challenge: a trust deficit, not simply a retrieval problem. Across 101 organizations, AI agents are delivering confident answers, yet more than half (57%) report instances of those answers being demonstrably wrong due to inconsistent or missing business context. This "context gap" highlights a need for a governed semantic layer – currently under construction for many – and a shift towards hybrid retrieval approaches.
Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
Unlock the full potential of Retrieval-Augmented Generation (RAG) with Context Engineering for Question Parsing. This approach transforms raw, unstructured questions into precisely typed fields, directly steering both retrieval and generation processes. Published in Enterprise Document Intelligence [Vol.1 #6quater], this post details a critical technique for maximizing AI agent effectiveness. Addressing the "AI context gap," as explored in our related article, "The AI context gap: Enterprise AI organizations have a trust problem…", this method ensures your AI agents operate with clarity and precision.
Building Trustworthy Production RAG Systems Through Continuous Evaluation
Production Retrieval-Augmented Generation (RAG) systems demand ongoing vigilance to ensure reliability. Our practical guide, "Building Trustworthy Production RAG Systems Through Continuous Evaluation," details a workflow to proactively identify and rectify retrieval failures, hallucinations, and performance drift—before they impact users. This approach prioritizes continuous assessment, establishing a robust feedback loop for optimal system performance. For deeper insights into evaluation methodologies, explore "Don’t Let Claude Grade Its Own Homework," which examines cross-provider PR review strategies.
Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
RAG (Retrieval-Augmented Generation) hallucinations aren't primarily model flaws; they're overwhelmingly retrieval failures. Enterprise Document Intelligence, Vol.1 #7quinquies, reveals that the retrieval component—the “brick” selecting context—is often the root cause. Simply put, garbage retrieval leads to garbage output. Addressing retrieval shortcomings is the most impactful step toward mitigating hallucinations, as it limits the model’s opportunity to invent information. As Vint Cerf explores with his work on identifying AI agents, ensuring reliable data sources is paramount.