Prompt Optimization Misses Deployment Layer

This is the right diagnosis for most production LLM pain: prompt quality matters, but reliability is usually decided by the wrapper around the model.

• –Output can be locally correct and still fail once it hits real context, state, or timing constraints
• –Test and production drift turns “works on my prompt” into a false sense of reliability
• –The fix is usually evals, tracing, schema validation, and rollbackable configs, not more prompt polish
• –Teams need to measure downstream task success, not just model response quality
• –This is where observability and workflow design start mattering more than prompt craft