Inventor's Paradox

Background Context is everything when working with LLMs. Here are five practical levers that determine whether your agent session succeeds or wastes a lot of tokens. This article explores the minimum amount of context you need to provide to an LLM in order to perform a given software development task. When answering a user's prompt, an agent harness (like Claude Code or Cursor) can perform additional work such as reading files, exploring trial solutions, and updating existing source code. We want the LLM — together with its harness — to succeed at whatever task we provide, but we need to be thoughtful about the context we give it. Context 1: Project Folder For the LLM to efficiently reach application source code, database schemas, table structures, and markdown documents, it is best to start the agent harness inside the project folder, or just by specifying the project folder for the conversation. The harness will allow the LLM to list directories, search for keywords within the pr...

The LLM as Teammate: Rethinking Software Development In this era, Artificial Intelligence engines now help people build software. I am not talking about embedding AI capabilities into existing applications — that is another topic altogether — but about using AI to write software itself, removing the programming language barrier for the person building it. In the past, if we needed to build software with a particular programming language, we needed to be proficient in it, or we needed to know someone who was. With AI, I could be less proficient in a platform yet still build something meaningful with it. As long as the LLM has proficiency in that programming language, all is well. And with the LLM joining as part of the "programming team," the limitation of working hours almost vanishes. If I still have sufficient LLM token quota, even starting some coding work at 11 PM is possible — no need to wake a programmer for a task. In some respects that is a win, but in others we get l...