In data analytics, we’re facing a paradox. AI agents can theoretically analyze anything, but without the right foundations, they’re as likely to hallucinate a metric as to calculate it correctly. They can write SQL in seconds, but will it answer the right business question? They promise autonomous insights, but at what cost to trust and accuracy?
These days, everyone is embedding AI chat in their product. But to what end? Does it actually help, or would users rather turn to tools like Claude Code when they need real work done? The real questions are: how can we model our data for agents to reliably consume, and how can we use agents to develop better data models?
After spending the last year exploring where LLMs have genuine leverage in analytics (see my writing on GenBI and Self-Serve BI), I’ve identified three essential pillars that make agentic data modeling actually work: semantics as the shared language both humans and AI need to understand metrics, speed through sub-second analytics that lets you verify numbers before they become decisions, and stewardship with guardrails that guide without constraining. The TL;DR? AI needs structure to understand, humans need speed to verify, and both need boundaries to stay productive. — Read More
Daily Archives: October 20, 2025
Advanced RAG Techniques for High-Performance LLM Applications
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by combining retrieval with generation to ground outputs in your own data rather than relying solely on pretraining. In practice, RAG systems retrieve relevant information from a knowledge source and integrate it into the prompt, enabling responses that are more accurate, contextual, and trustworthy.
RAG is now a widely used architecture for LLM applications, powering everything from question-answering services that leverage web search, to internal chat tools that index enterprise content, to complex QA pipelines. Its appeal is simple: by augmenting generation with retrieval, teams can deliver LLM experiences that meet today’s expectations for relevance and reliability.
But shipping a RAG system isn’t the finish line. Anyone who’s moved beyond a prototype knows the symptoms: hallucinations creep back in, long queries bog down performance, or answers miss the mark despite the right documents being retrieved. That’s where advanced RAG techniques come in. This guide walks through the strategies that help teams improve relevance, accuracy, and efficiency, so your system not only works, but works at scale. — Read More
Emerging Architectures for Modern Data Infrastructure
The growth of the data infrastructure industry has continued unabated since we published a set of reference architectures in late 2020. Nearly all key industry metrics hit record highs during the past year, and new product categories appeared faster than most data teams could reasonably keep track. Even the benchmarkwars and billboard battles returned.
To help data teams stay on top of the changes happening in the industry, we’re publishing in this post an updated set of data infrastructure architectures. They show the current best-in-class stack across both analytic and operational systems, as gathered from numerous operators we spoke with over the last year. Each architectural blueprint includes a summary of what’s changed since the prior version.
We’ll also attempt to explain why these changes are taking place. We argue that core data processing systems have remained relatively stable over the past year, while supporting tools and applications have proliferated rapidly. We explore the hypothesis that platforms are beginning to emerge in the data ecosystem, and that this helps explain the particular patterns we’re seeing in the evolution of the data stack. — Read More
Rick's Cafe AI 