How digline compares¶
The space around "testing LLM applications" is crowded, and most tools in it are good at what they do. The useful question is not which tool is best, but which question each tool answers. There are three.
"Did the output change?" — snapshot and replay testing¶
Tools like proveai and EvalView record your application's outputs and detect when they differ from a recorded snapshot, typically via content hashing or diffing against a golden baseline. This works well for deterministic pipelines: if the output should be identical every time, any difference is a signal.
LLM outputs are rarely identical every time. With sampling, a hash tells you that something changed — which you already knew — not whether it got worse. And a diff between two long generated texts tells you where the words differ, not whether the new answer still meets the bar you approved. Change detection answers a question that sampled outputs make nearly meaningless.
"How is it going in production?" — observability¶
Langfuse, LangSmith, Arize Phoenix and similar platforms trace your application in production: latency, cost, user feedback, judge scores over live traffic. This is valuable — it tells you how the system is doing now, on real inputs, after deploy.
What observability cannot tell you is whether the change you are about to ship makes things worse, because it only sees traffic after the change is live. It is a rear-view mirror — an essential one — while regression testing is the check before you pull out.
"Did it get worse than what I approved?" — digline¶
digline answers the pre-deploy question, and takes the statistics of LLM outputs seriously:
- Your cases and assertions live in a suite.py; scores come from checks and, where judgment is needed, an LLM judge.
- When results are good, you promote them: the approved scores — together with the prompt and the commit that produced them — become a versioned baseline in your repo. Not a hash: numbers, with tolerances.
- On every change, digline compare tells you which case got worse, and by how much. "Was 0.91, now 0.78, still above threshold" is a first-class verdict — not a passed check, not a wall of diff.
- Because an LLM judge is itself noisy (in our measurements, a judge flips its verdict on roughly 1 case in 20), digline is built to separate signal from noise: sampled runs, aggregate scores, tolerances — so a flip doesn't fail your build and a real degradation doesn't hide in the variance.
Two things digline will never do, by design: no hosted service that receives your payloads, and no data collection. The baseline lives in your repo; the runs happen on your machines. For teams whose prompts and outputs cannot leave their perimeter, this is not a feature toggle — it is the architecture.
Frameworks like promptfoo and DeepEval¶
promptfoo and DeepEval are excellent at exploration: comparing prompts, models and configurations side by side, with rich metric libraries. digline is deliberately narrower — it doesn't help you find the best configuration; it guards the one you approved. Many teams will use an exploration framework to choose, and digline to hold the line afterwards.
Using them together¶
These families complement each other. A reasonable production setup is: an exploration framework while developing, digline as the gate in CI before deploy, and observability watching live traffic after. digline's only strong opinion is about its own layer: the verdict on regressions belongs in your repo, next to your code, before the deploy — never in someone else's cloud.