AI Engineering Blog
Practical Notes for Building AI Products
Production-focused writing on RAG, OpenClaw, n8n automation, MCP integrations, and vector database performance. Every post is written for founders and engineering teams shipping real systems.
Featured Article
The Engineering Playbook for Production-Grade RAG Systems
Beyond the basic tutorial: A deep dive into chunking strategies, embedding selection, hybrid search, and evaluation loops that actually ship to production.
🤖 AgentsAgentic Orchestration: Building Reliable Multi-Agent Systems with LangGraph
How to move beyond simple 'agent' prompts to robust, state-managed orchestrations that can handle complex business logic and long-running tasks.
🧩 n8nn8n for Enterprise: Building AI Workflows That Actually Scale
A deep dive into why n8n is the 'secret weapon' for AI engineers building complex automation pipelines without the maintenance headache.
🔌 MCPMCP in Production: The New Standard for Model-Tool Communication
Model Context Protocol (MCP) is the 'USB-C for AI'. Here's how to build a production-ready MCP server to expose your internal data to any LLM host.
🗂️ Vector DBVector DB Tuning: Scaling to Millions of Embeddings with HNSW
A deep dive into the math and engineering behind HNSW indexing, and how to tune your vector database for the optimal balance of speed and recall.