Rag | martinuke0's Blog

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.

Architecting Distributed Vector Databases: Scaling Semantic Search from Prototype to Production

How to design, partition, replicate, and operate a distributed vector database for semantic search at scale — covering sharding strategies, HNSW vs. IVF, hybrid retrieval, and operational pitfalls.

Diagram of a multimodal RAG pipeline linking image encoder, vector store, and LLM.

Architecting Multimodal RAG Pipelines: Integrating Vision-Language Models for Production-Ready Applications

A deep dive into building production‑grade multimodal RAG systems, covering architecture, data flow, scaling, and monitoring with real‑world examples.