2h ago · 42 min read · Introduction Prompt engineering was, until very recently, the dominant lens through which practitioners understood how to work with large language models. Write better prompts, get better outputs. Tha
Join discussion
1d ago · 12 min read · Your agent just produced a confident answer. It sounds plausible. It cites the right kind of context. It may even have called the right tool. But something feels off. The answer is shaped by a viewpoi
Join discussion3d ago · 13 min read · AI coding assistants have moved far beyond autocomplete. They now generate functions, refactor systems, write tests, debug complex issues, and assist with architectural decisions. But despite rapid pr
Join discussion
4d ago · 3 min read · The article made one thing very clear. Agents do not fail because models are weak. They fail because context is poorly managed. Context engineering is about deciding what information lives inside the
Join discussion5d ago · 14 min read · If you’ve built a RAG (Retrieval-Augmented Generation) system in the past year, you’ve probably hit the wall where your LLM returns confidently wrong answers, cites information that doesn’t exist, or
Join discussion
May 20 · 14 min read · A team from Stanford, SambaNova, and UC Berkeley recently published the ACE paper - and it's the most substantive academic contribution to context engineering I've seen in a while. The core idea: give
Join discussion
May 19 · 3 min read · Beyond Prompts: The Rise of Context Engineering Artificial Intelligence has advanced rapidly, and the way we interact with it is evolving. A year ago, “prompt engineering” was the dominant strategy: c
Ffor commentedMay 10 · 8 min read · The Builder Who Builds Too Fast Imagine you're hiring a contractor to build your house. You get three bids. Two of them say six months. One says three weeks. Your gut reaction to the three-week bid? Something's wrong. They must be cutting corners. Th...
Join discussion
