Knowledge Base
Knowledge Base on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on knowledge base 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 knowledge base, 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 Listing Questions: When the Answer Is Every Passage, Not the Top One
Many Retrieval-Augmented Generation (RAG) pipelines face a silent challenge: handling listing questions—those requiring answers synthesized from multiple passages, not just a single top result. Loop Engineering’s latest article, "Loop Engineering for Listing Questions," identifies this critical category and introduces a refined pipeline architecture designed to address it effectively. Discover how this approach empowers more accurate and comprehensive responses, transforming how you leverage document intelligence. For a broader perspective on data exploration tools, see our comparison of Matplotlib and Plotly.
Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer
Retrieval-Augmented Generation (RAG) systems often fall short when answers direct users to other sections of a document instead of providing the information directly. Loop Engineering addresses this common challenge with a crucial refinement: enabling pipelines to loop back and retrieve linked context. This ensures users receive complete answers, transforming the RAG experience from frustrating redirection to seamless knowledge access.
How to Build a Context Layer and a Company Brain
Transforming scattered company knowledge into a reliable resource for LLMs requires more than just a demo—it demands a structured context layer and company brain. This post clarifies what it *actually* takes to achieve this, revealing the demo represents only a small fraction (around 5%) of the total effort. We’ll outline the essential components and practical steps for building a system that empowers AI with your organization's unique data.
Multi-Tenant SaaS: Which Architecture Would You Choose? [D]
Navigating multi-tenant SaaS architectures for sensitive data, particularly with RAG and LLMs, demands careful consideration. For your Sri Lankan document platform, a global RAG layer alongside user-specific RAG (Option 1) presents a compelling starting point. It avoids the complexities and costs of fine-tuning while enabling access to a curated knowledge base for accurate, general responses, supplemented by private document search. Scalability to thousands of users is readily achievable with this design.