context engineering
context engineering 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 context engineering 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 engineering, 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).”
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
The conversation around loop engineering often centers on Large Language Models (LLMs), but can the architecture itself drive improved performance? This article presents a novel experiment, rigorously testing a deterministic, zero-dependency Python benchmark to isolate failures—without an LLM. Results across 300 random seeds demonstrate that goal-directed controllers consistently outperform linear pipelines in completing independent branches. Explore the architecture, benchmark, and debugging process, revealing that failure isolation is, in fact, a measurable property of control flow.
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.