Build your baseline of truth with North Star
What you can measure, you can understand.
What you can measure, you can understand.
Turn what product writes into artifacts engineering can build and everyone can measure.
Distill your requirements into a seed, grow it into a dataset you can build toward and measure against.
Each criterion becomes a grader, so every result is scored against what you defined as “good”.
Your vision becomes the bar those grades are read against, so progress is a number, not a feeling.
From a doc everyone reads once to a charter everything is measured against.
The PRD was the single source of truth. Goals, users, and stories spelled out in prose. The whole team read the same doc, aligned on intent, and engineering built against it.
Prose can't check anything. The doc can't tell you whether an AI output is on target. What good looks like stays implicit, so every reviewer interprets it differently.
North Star distills the PRD into a golden seed: the task's input, its output, and what a good output must satisfy. Paste a PRD and the seed pre-fills itself. From there it anchors everything downstream: dataset, scorers, evals.
From a checklist walked at sign-off to graders that run on every change.
Acceptance criteria defined done for each story. A checklist agreed between product and engineering, it settled scope before building and converted straight into QA test cases.
Binary pass or fail doesn't survive AI. The same prompt gives different outputs every run, and the quality you care about can't be written as one expected answer. Checking once fails when every change shifts the results.
Each criterion becomes a scorer: same intent, now executable. An LLM judge grades every output in the dataset against it, and grades again on every change. A one-time gate becomes a living measure of good.
From a direction you point at to a yardstick you score against.
The vision set the direction. It rallied the team and stakeholders around where the product is headed, and it justified the roadmap and its bets.
Better and smarter have no number behind them. Each release gets judged on vibes, regressions slip through, and the goal never touches the day-to-day work.
The vision becomes a benchmark: a labeled dataset plus scorers that make good measurable. Every change is scored against the same yardstick. Progress is a number, and regressions show up across the whole dataset.
The same seed and scorers from each side of the team, and the ROI both can point to.
Own the definition of good and see the scores, no code required. Prove the feature moves the metric, and prioritize by impact.
The product view →Build the agentic harness once and reuse it across features. Monitoring and alerts built in, and labeled data that compounds into smaller, cheaper models.
The engineering view →