Building with LLMs shouldn't mean duct-taping together a spreadsheet of test prompts, a notebook for scoring, and a logging dashboard nobody trusts. devx-ai gives AI builders one fast, connected loop — dataset, eval, compare, ship, trace, iterate — so you spend your time building, not context-switching between five tabs.
It's the tooling layer between "the prompt works on my machine" and "we trust this in production" — built for the way AI builders actually iterate.
One loop, from first prompt to production and back again.
Make your feature's spec explicit — the questions it needs to answer, and what a correct answer looks like.
Run your dataset against a model and every test case comes back scored — pass or fail, with a reason.
Diff a baseline and a candidate run to see precisely what got better and what got worse.
Trace every production call, get alerted on threshold breaches, and feed what you learn back into the dataset.
Ten sections covering the full loop — from golden datasets to production observability.
Pass-rate, latency, and cost trends across every evaluation run at a glance.
Run golden datasets against metrics like relevancy, faithfulness, and hallucination.
Diff two runs side-by-side to catch regressions before they ship.
Build and edit golden test case collections used to drive evaluations.
Version prompt templates across draft, staging, and production.
Inspect individual LLM calls — prompt, completion, latency, cost, and spans.
Human review queue for labeling traces and evaluation results.
Threshold monitors on pass rate, latency, cost, and error rate.
Usage and cost breakdowns by model, route, and time.
Test prompts against models and compare outputs interactively.
Click through a few sections below, or open the real thing in the preview.
Open the app to a live read on how your LLM feature is actually doing.
No sign-up needed — the preview runs entirely on local, seeded data.