Real Benchmark: 5 Chunking Strategies in Amazon Bedrock Knowledge Bases

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<!-- SC_OFF --><div class="md"><p>Projects are <a href="https://github.com/snap-research/locomo/issues/31">still submitting new scores on LoCoMo as of March 2026.</a> but the

<blockquote> <p>A controlled study finds that data distribution, not just volume, dictates LLM capability. Benchmark-aligned training inflates scores but creates narrow, brittle

<p>This is the data appendix for <strong>Posts 1–4</strong>.<br /><br /> The narrative and takeaways live in the main posts. This page is pure measurement.</p> <p><strong>Eval

<h1> RAG Systems in Production: Building Enterprise Knowledge Search </h1> <h2> Introduction </h2> <p>Retrieval-Augmented Generation (RAG) has revolutionized how enterprises build

<p>When I started designing the RAG architecture for the internal AI assistant project, one of the first decisions I had to make was:</p> <blockquote> <p><strong>Where should the

<p>Most RAG retrieval problems I've debugged came down to the same thing: someone swapped the embedding model three times, added a reranker, then gave up — and never once changed

<!-- SC_OFF --><div class="md"><p>I tested two llama.cpp builds on the same <strong>Qwen3.6-27B-NVFP4</strong> model.</p> <p><code>llama-bench</code> reports the model label as