context
context 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 context 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 context, 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.
At VB Transform 2026, Zillow's engineering chief said AI ROI numbers only hold up if you measure before you build
At VB Transform 2026, Zillow's engineering chief, Toby Roberts, underscored a critical lesson for enterprise AI: establish measurement baselines *before* implementation. Zillow’s experience revealed that context, not just raw data, presents the most significant challenge when building AI architecture to support customers navigating complex real estate transactions. Their solution—a persistent context layer—demonstrates the value of owning this layer, alongside partners like Glean, to streamline workflows and optimize costs by leveraging smaller, task-specific models.
Interactive map of GPT-2's token embedding space - tap any token and explore [P]
Explore the intricate landscape of GPT-2's token embeddings with this interactive map, a compelling visualization of 32,070 alphabetic tokens from GPT-2-small. Accessible on mobile, the tool allows users to tap any token and discover its nearest connections, effectively "walking the graph" through real nearest-kin relationships identified via a minimum spanning tree. This innovative display, submitted by /u/Limp-Contest-7309, offers a unique perspective on language model structure—a deeper dive into GPT-2's vocabulary is available in our related article, "GPT-2 Small’s embedding geometry around “Trump.”
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).”
Prepare These 5 Assets Before Your AI Agents Take On More Work
Ready to empower your AI agents to handle more work? Success hinges on thoughtful preparation. Before scaling AI adoption, prioritize defining recurring tasks, providing the right contextual data, and establishing clear benchmarks for high-quality output. Critically, determine where human judgment remains essential. These five assets are foundational. As Amazon’s AGI director recently highlighted, reliability—not just capability—is key to enterprise AI deployment; explore deeper insights on this challenge in "Amazon AGI director says AI agent reliability…”.