Rag | martinuke0's Blog

Architecting Retrieval-Augmented Generation Systems: A Deep Dive into Production Pipelines

How to design Retrieval-Augmented Generation pipelines that hold up in production — covering indexing, hybrid retrieval, reranking, evaluation harnesses, and the operational realities of freshness, latency, and cost.

Layered architecture diagram of an AI agent stack spanning perception, reasoning, tools, memory, and action layers.

The Complete AI Agent Architecture: From Perception to Action

A field guide to designing production AI agents — from perception and reasoning loops to memory, tool routing, and the orchestration patterns that keep them reliable at scale.

Diagram-style illustration contrasting retrieval and cache flows feeding into an LLM.

RAG vs CAG: What the Difference Actually Means in Production

A practical, architecture-first comparison of Retrieval-Augmented Generation and Cache-Augmented Generation, with patterns for combining both in production LLM systems.

Abstract circuit board with neural network nodes glowing in blue.

Learning AI Engineering from Scratch in 2026: A Production-First Roadmap

Skip the tutorials and learn AI engineering the way it actually ships in production: pick a stack, build small, evaluate honestly, and deploy behind an API. Here’s the roadmap.

Diagram of a RAG pipeline with ingestion, embedding, vector store, and LLM generation stages.

Architecting Production-Ready Retrieval-Augmented Generation Systems

How to build RAG systems that survive real production traffic: chunking strategies, hybrid retrieval, vector store selection, caching, and the operational patterns teams actually ship.