ai-product-manager-master

To set up LLM product evaluations, start with error analysis first, then establish binary pass/fail criteria with judge calibration. You must define a golden dataset and use eval-driven iteration to identify trace-driven failure mode taxonomies before reaching production ship gates.

The difference between a workflow and an agent lies in capability boundaries and autonomy. You diagnose workflow-vs-agent fit by evaluating model capability shifts, reliability constraints, and pass^k criteria to determine if a deterministic process or autonomous model selection is needed.

You decide to upgrade RAG product architecture by scoping real failure modes through error analysis. If eval-driven iteration shows persistent failures beyond prompt or retrieval changes, you must shift to a deeper architectural redesign or transition to a workflow-to-agent boundary.

Pass^k reliability criteria for agent shipping gates is a production-focused quality control that enforces consistent agent performance across multiple iterations. It ensures your LLM product meets trace-driven failure mode taxonomy requirements and token-cost constraints before deployment approval.

To manage token cost constraints when iterating on LLM features, start with the cheapest model and upgrade complexity only when needed. This approach enforces error analysis first and aligns with model selection rules to prevent overspending during eval-driven iteration cycles.