Weak LLM output usually traces back to thin input. Here’s what good context includes, a before-and-after prompt example, and a five-point checklist. Most people unhappy with their LLM output share the same complaint. It's generic. It sounds like everyone else. It misses the point.They usually blame the model. In my experience, the model is rarely the problem. The input is.I've written more than 2,500 articles and helped train and evaluate language models for Google and Meta. The pattern is the

Your assistant isn’t “forgetful.” Your workflow is. Here’s a repo-first system: mistake memory, patch-based retrieval, and regression gates that stop relapses.

In fAI, git-backed context keeps your AI's understanding of your work in a plain git repo on your machine: what it holds, and what owning it actually buys you.

Overcome AI Blank Page Paralysis. Learn why empty prompt boxes cause vague results and how structural constraints transform intentions into clear outputs.

AI’s memory got bigger, so what you put in it matters more than ever

What Is Context Engineering for Generative AI Models? Introduction Generative AI can write content, answer questions, summarize documents, and automate tasks. However, even a powerful AI model can pro...

No AI, however smart, can guarantee that a change breaks nothing that worked before. With agents making more changes, regression tests matter more, not less.