LLM

Embedding model choice is a 5% problem for most RAG systems. Your chunking strategy is the 50% problem. Here's how to pick anyway.

Your AI feature has a 200-line system prompt living in a string in app.py. That's tech debt. Here's how to treat prompts like first-class artifacts.

Prompt caching is not a 90% discount. It's a 90% discount on the static parts only. Here's how to actually compute your cache savings.

Your AI feature passes 100% of unit tests and ships broken to users every other week. Here's why, and how to actually test LLM-powered systems.

Claude 4 didn't get stupider. Your safety layer is failing. How to identify when the problem is your architecture, not the LLM.

Ten months after MCP went multi-vendor, most teams are still treating it as a nicer function-calling wrapper. That's the wrong mental model — and it's quietly producing architectures that don't scale.

A MAST taxonomy of 1,600+ execution traces maps 14 failure modes across 3 root causes. The model is almost never the problem. The orchestration architecture almost always is.

82% of AI teams say prompt engineering alone isn't enough. The ones succeeding in production are treating context design the same way they treat database indexes — as an architectural decision, not a prompt trick.

Managed inference APIs are convenient until they are not. Here is the full picture of running your own LLM on Kubernetes: GPU scheduling, model storage, vLLM vs Ollama, and the operational tradeoffs.

LLMs don't know your data. RAG fixes that by turning your documents into a searchable knowledge base. Here is the full pipeline: chunking strategies, dense vs hybrid retrieval, re-ranking, and when to reach for graph-based RAG with LightRAG.