Stack Innovations
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Stack Innovations
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Every model and prompt change graded against a frozen golden set before it ships — accuracy, safety, format, latency — with an LLM-as-judge, a regression gate in CI, and tracing on every run. No "looks good in the demo." A number that moves, or it doesn't merge.
A ledger of named systems where the line that moved was a regression caught before users felt it — pass rate, safety, latency budget, drift held in check. 6 of 30 shown · ledger updates as suites grow.
A frozen set of test cases, scored across four dimensions as pass/fail cells. Switch model/prompt versions and watch the grid recolour — v2 lifts accuracy, v3 regresses safety. Move the threshold and the gate decides: ship, or block. This is what "evaluated" actually looks like.
Confidence hides in the stages between a code change and a release — what you grade against, who judges, how the gate decides, what you trace in production. This is the room we work in: each stage measured, each tool chosen for a reason.
Before a single test runs, we pin down what "good" actually means for your system — which answers are right, which are unsafe, what format the product needs, and the latency users will tolerate. Vague quality goals make vague evals.
Pull real inputs from tickets, logs, and edge cases, label the expected behaviour, and freeze it. A frozen set is the whole point — scores only compare if the questions never move underneath you.
Some answers grade themselves — exact match, schema valid, latency under budget. The open-ended ones need a judge: Claude scoring against an explicit rubric, calibrated against human labels so the grade is trustworthy.
The eval suite runs in CI on every change. Pass rate above threshold and no dimension regressed — it ships. Below the line — it blocks, with a diff showing exactly which cases broke. The gate, not a gut feeling, decides.
In production, every request carries a trace — prompt, retrieved context, tool calls, tokens, latency. When something looks wrong, you replay the exact run, not guess from a screenshot. Observability is what makes a regression debuggable.
Live traffic is sampled and judged continuously, scores plotted over time. When quality dips week-over-week — a model update, a data shift, a new edge case — an alert fires before a user files the ticket. Evaluated once isn't evaluated.
Every caught regression feeds the next: each production failure becomes a new golden case, each near-miss tightens a threshold, each drift alert sharpens the suite. Ship-and-pray AI decays silently as models and data shift. Evaluated and gated, quality holds — and the suite that holds it keeps getting stronger.
The frameworks, judges, and infra we actually wire together to grade, gate, trace, and monitor AI in production. No mystery harness — just the kit that keeps quality measured.
A free eval audit to start — bring a model or prompt you ship today and a set of real cases, and we'll build a golden set, run it through an LLM-as-judge, and show you exactly where quality is silently slipping. A scorecard, not a pitch.
Get an eval audit →