7 results for “RAG evaluation”

Your AI Team Is a Bottleneck: Why LLMs Belong on the Platform

Stop creating siloed "AI teams" that become bottlenecks. To scale LLM development, treat AI capabilities as a product built by your platform team, enabling product teams to innovate faster and more safely.

Apr 23

Most Enterprise RAGs Have No Access Control: How to Fix Yours

Vendor research shows most enterprise RAG deployments lack basic access controls, creating major data leak risks. This article details how to fix it by implementing pre-retrieval filtering in your vector database to enforce permissions before data reaches the LLM.

Oct 07

Why Your RAG Pilot Won't Survive Production

A RAG pilot on 200 curated documents looks magical, but scaling to 200,000 uncurated ones in production causes it to break. This article explains why recall collapses, how to fix a failing RAG pipeline, and what you should be measuring instead.

Dec 23

The GenAI Divide: Why 95% of Enterprise AI Projects Delivered No Value

In 2025, enterprises spent $37 billion on generative AI, yet a stark "GenAI Divide" emerged. A landmark MIT study found 95% of projects failed to deliver any business value, revealing that success depends not on model quality, but on organizational strategy.

Feb 10

Your LLM Judge's Secret Drift: Why 80% Agreement Isn't Enough

LLM-as-a-judge provides scalable evaluation but suffers from silent drift as models and rubrics change. This guide explains how to use a human-labeled holdout set to calibrate your judge, diagnose regressions, and prevent your evaluation system from misleading you.

Oct 17

Chunking Is a Retrieval Decision: Up to 9% Recall Is on the Line

Most RAG systems fail due to a single, overlooked setting: chunk size. This article explains how rethinking chunking as a dynamic retrieval parameter, not a static preprocessing step, can boost recall by up to 9% and why the optimal size depends entirely on your users' questions.

Apr 02

The 76% Mistake: Why AI Build vs. Buy Is a Layer-by-Layer Choice

The "build vs. buy" decision for LLM applications is no longer a single choice. This article presents a framework for analyzing the AI stack layer by layer—from the model to evaluation—to avoid common pitfalls and focus engineering effort where it creates the most value.