RAG pipeline
RAG pipeline at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Accelerate Local LLM Learning: A New Prototype for Faster Fact Correction”, “Route easy queries around the model to cut latency and cost”, and “Four PDFs, one pipeline: consistent, cited answers from messy documents”. Watching a toddler learn taught Kavanutz something about AI. Every question in an enterprise RAG pipeline doesn't deserve a full model call. 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 RAG pipeline story on Beyond Market Intelligence, newest first.
Accelerate Local LLM Learning: A New Prototype for Faster Fact Correction
Watching a toddler learn taught Kavanutz something about AI. Jayce, his new prototype, skips the heavy lifting of RAG pipelines and fine-tuning entirely. Instead, it shifts raw context vectors across a fixed pool of 4,048 prototype slots, correcting mistakes in real time. The benchmarks are telling: faster updates, better sample efficiency on sequential tests, and a strict memory ceiling. It's a lean, framework-free proof of concept built on NumPy and Java.
Route easy queries around the model to cut latency and cost
Every question in an enterprise RAG pipeline doesn't deserve a full model call. The pipeline from Article 9 proves that simple keyword matches can skip the LLM entirely, saving about two seconds per query. That's not a small win; it's a direct cut to latency and cost without sacrificing accuracy. Buying faster hardware isn't the only answer. Routing easy questions around the model is smarter. For more on questioning AI's assumptions, check out "Verify Your AI's Understanding: A Simple Check for Tax Season."
Four PDFs, one pipeline: consistent, cited answers from messy documents
Four very different PDFs. One RAG pipeline. The same four upgraded bricks in every case, and every answer typed and cited. That is the kind of consistency that makes a tool feel trustworthy, not just clever. We appreciate how the demonstration walks through the practical wiring of a paper, a NIST standard, and a report with a broken TOC. It is a grounded demonstration of what happens when retrieval is built to handle real-world mess.