Knowledge Base
Beyond Market Intelligence keeps Knowledge Base in one place: 4 stories so far. The section currently leads with “When Every Passage Holds the Answer, RAG Needs a New Shape”, “When your document says "see Section 7.2," AI can now follow the link.”, and “From Scattered Knowledge to an AI-Ready Company Brain”. Most RAG pipelines retrieve a single top passage and call it a day. A "see Section 7.2" answer is a dead end, not a destination. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Knowledge Base story on Beyond Market Intelligence, newest first.
When Every Passage Holds the Answer, RAG Needs a New Shape
Most RAG pipelines retrieve a single top passage and call it a day. But some questions don't work that way. Listing questions demand every relevant passage, not just the highest-scoring one. This pipeline walks through that silent failure point and the pipeline shape that actually handles it. It's a practical look at a real limitation, and the reasoning is clear. For readers who want to go deeper, "Exploring Paragraph Structure: How LLMs Navigate Token Space" offers a useful follow-up on how token coordinates shape retrieval.
When your document says "see Section 7.2," AI can now follow the link.
A "see Section 7.2" answer is a dead end, not a destination. When a retrieval system points elsewhere instead of answering directly, the pipeline breaks trust. This piece tackles that exact failure with a loop-based fix: fetch the linked context, then return the real answer. It's a practical, human-centered solution for a frustrating problem. We appreciate the focus on user outcomes over technical spectacle. For more on how LLMs handle structure, explore *Exploring Paragraph Structure: How LLMs Navigate Token Space*.
From Scattered Knowledge to an AI-Ready Company Brain
Building a context layer sounds straightforward until you try to move beyond a demo. This piece cuts through the hype to show what it actually takes to turn scattered company knowledge into something an LLM can reliably use. The polished demo is only about five percent of the real work. The rest is messy, unglamorous, and essential. If you are weighing whether your team is ready for this, it is a sobering and useful read.
Choose the Right Architecture for Your Multi-Tenant Data Platform
Choosing between fine-tuning and a shared RAG layer isn't about sophistication; it's about where truth lives. Your instinct on Option 1 is sound. Fine-tuning is costly and slow to update, while a curated global knowledge base gives you control over accuracy and citations. For Sri Lankan context, pair a global RAG with your user-specific stores. This scales cleanly and keeps sensitive data isolated. For deeper architectural trade-offs, our piece on "Exploring Paragraph Structure" offers useful context on how LLMs navigate token spaces.