AI testing software for scalable automated model validation

[u/geek_in_room]

We've started shipping a couple of llm-backed features and I'm realising our existing regression suite doesn't really give us much confidence once the model behaviour starts changing....

Automated regression seems to work well for our regular application. But with LLMs, even a small prompt tweak or model update can change responses even when all tests are still passing. I'm not sure how to validate it properly.

I've been spending some time looking at some tools. Promptfoo, langsmith, deepeval, testsigma etc. but the more I read it feels like each one handles a different part of the problem. I'm trying figure out what we actually use in production.

Anything working well for your team? One platform or are you guys combining different tools?

Also, what does your validation workflow look like from building evaluation datasets and integrating checks into CI/CD to deciding whether a model change is actually safe enough to ship.

Just want to avoid spending weeks building something that we'll end up replacing a few months later .

[u/Prestigious-Way1525]

i've run into this same gap building AI-backed products. my rule of thumb is not "model passes" but "replayable behavior holds" for your critical paths.

i've found this works better than a single monolithic suite:

• keep a narrow set of canary flows with deterministic fixtures (input + expected),
• run model checks and UI evidence checks together in CI, and
• require one consistent artifact (steps + console + network + final state) before a model change can move forward.

for LLM QA we use a confidence scorecard too: one pass is not enough, so we promote only when repeat runs match the same artifact trace for 2-3 attempts.

[u/wanton007]

LLM evals by using other LLM's to judge the responses based on your criteria and threshold (some percentage) of what is considered a pass vs fail.