Written after the deployment, not before.

A comprehensive guide to end-to-end LLM system design architecture, covering the core patterns like RAG, vector databases, and agentic workflows required to ship production-grade AI applications.

Developers often try to add knowledge to a large language model by fine-tuning it, a costly and often ineffective approach. A better strategy follows a clear hierarchy: start with the prompt, then use Retrieval-Augmented Generation (RAG) for facts, and only use fine-tuning to change the model's core behavior.

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.

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.