Every week I watch someone describe the same AI workflow problem in a different disguise. The model forgets. The model hallucinates. The model confidently rewrites code that was fine.
Most of these aren't model problems. They're operating problems — how you set up the conversation, how you hand over context, how you hand off between sessions.
Context is a resource, not an ambient gas
Treat the context window like you treat RAM on an embedded device. Every token you burn on filler is a token that isn't available for reasoning. Write dense. Cut pleasantries.
// before
"Could you please help me understand..."
// after
"explain:"
Persistence belongs on disk
The conversation is ephemeral. If a fact matters for tomorrow, it goes in a file — CLAUDE.md, AGENTS.md, a memory note. If a decision matters for the next PR, it goes in the spec.
Clarity comes from constraints
Give the model a role, a goal, a set of things it may not touch, and a definition of done. Vague prompts produce vague output. This is not a model limitation. It's physics.