Brick
Brick on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on brick 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 brick, 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.
FAQ as RAG: When You Get to Design the Corpus
Traditional Retrieval-Augmented Generation (RAG) pipelines are fundamentally rethought in "FAQ as RAG." This innovative approach, detailed in Vol.1 #B2, prioritizes corpus design, simplifying parsing and transforming retrieval into a caching mechanism. Critically, few-shot prompting is redefined as a retrieval challenge. This represents a significant shift for enterprise document intelligence. Explore this transformative model and discover how it empowers more efficient and accurate AI applications – a concept further explored in "Your LLM Can Return Perfect JSON and Still Be Wrong."
Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
RAG systems, while promising, frequently produce inaccurate outputs. Our latest research, "Most RAG Hallucinations Are Extraction Errors," reframes this issue, clarifying that many perceived “hallucinations” stem from flawed data extraction, not imaginative generation. We identify seven typed-contract patterns designed to ensure generation honesty, particularly valuable for smaller models. This decomposition rule significantly improves reliability. Addressing the AI context gap—as explored in our article on enterprise AI trust—is critical for realizing RAG’s potential.
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.