For foundational models, I feel like this is working against the model, they already are trained as experts, they need guidance, not prose about how to be an ex

For foundational models, I feel like this is working against the model, they already are trained as experts, they need guidance, not prose about how to be an expert # Effective steering stack: "FastAPI + SQLAlchemy + Redis" scale: "10k RPS, sub-50ms P99" deployment: "K8s, multi-region" constraints: ["async-first", "12-factor", "observability"] # Not this python-expert: "You are an expert in advanced Python..." # This context: "Building FastAPI backend, PostgreSQL, Redis cache, Docker deployment" constraints: "Sub-100ms response times, 10k concurrent users" preferences: "Async-first, type hints, structured logging" I stopped telling ai how to do their jobs a long time ago, and started context management, I get crazy better results. The only time i need to bash training in is when it doesn't know an API, then I spawn a research agent to create an updated training prompt for an API, or command, then import it as needed. Keeps the primary context window cleaner for longer.