That's a neat setup. I'm just starting to look at Freeplay's API for something similar, but with expense reports. How are you handling the authentication for the API call from your GitHub Action? Are you storing the API key as a secret? I'd be nervous about it being exposed in logs.
Still learning.
Valid JSON is just the baseline. If your system is at the point where invalid JSON is a common failure mode, you've got bigger problems.
The parsing bucket is for any check that's deterministic and doesn't require an LLM. So we're checking for the *presence* of specific keys in the JSON, that numeric values fall within an expected range, that a returned list isn't empty when it shouldn't be. It's cheap logic you'd write in unit tests, but applied to the LLM's output structure.
The real cost saver is moving any objective, rule-based validation out of the "judge" calls. If the spec says the response must contain a 'status' field, you don't need GPT-4 to tell you it's missing.
Beware of free tiers
Exactly. This separation is crucial for cost control and reliability. We treat our deterministic checks as a mandatory quality gate that runs before any LLM-based evaluation is even called.
Your point about checking for key presence is so important. One thing we've added is schema validation for any JSON output, using something like Pydantic. It catches type mismatches - like a number that's sent as a string - that a simple key check might miss, and it's still infinitely cheaper than a judge call.
The other benefit is speed. Those fast, rule-based checks can run in milliseconds and give immediate feedback to a developer. If they fail, we don't waste time and money spinning up a more expensive evaluation. It forces clarity in the spec from the start.
Architect first, buy later
Good find. Automating on merge is the right first step, but you're leaving value on the table if you stop there.
Scheduling nightly runs against a sample of *real* production queries is where you catch drift the manual process never will. Your test suite stays relevant because the data refreshes daily. We treat it like a recurring cost audit.
Also, make sure you're tracking the cost of those automated judge calls. It's easy for that to silently blow up if your validation logic is too loose.
—hd
Great discovery, and merging that into CI/CD is exactly where the API shines. A couple observations from our setup that might help refine yours.
We also run a smoke test suite on merge, but we had to introduce a gating mechanism. Triggering the suite is easy, but if your evals take more than a few minutes, your merge check can time out waiting for a result. We solved this by having the Action simply trigger the run and record the run ID, then a separate, async process monitors completion and updates the commit status. This keeps the CI pipeline moving.
Your point about scheduling based on production metrics is interesting. We haven't tied evals directly to metrics, but we do have a scheduled job that pulls a random sample of the previous day's user queries and runs them through a separate "real-world fidelity" suite. It's less about a specific metric threshold and more about continuous sampling to catch drift the static test set might miss. Have you thought about how you'd pick the triggering metric?
That's a smart use of the API. I'm just starting with this, so maybe this is obvious, but how do you actually structure the POST request? Is there a specific mutation you call?
Also, for the Slack notification, are you using the Freeplay result directly, or do you format it yourself in the Action? I'm worried about the output being too noisy if the suite has a lot of test cases.
The mutation you want is `createRun`, but the docs are a bit sparse on what goes in the `input` object. You'll be reverse-engineering the network calls from their UI, honestly.
For Slack noise, we parse the Freeplay result. Sending raw output is useless. We just report pass/fail counts, duration, and link to the run. If a test fails, we include the failure message. A suite with 50 cases becomes one readable Slack line, not a log dump.
Data skeptic, not a data cynic.
Reverse-engineering from the UI sounds frustrating. I hope they improve the API docs soon.
When you parse the result for Slack, do you find the Freeplay API response structure is consistent enough for that? I'm worried my parsing script would break if they change something.
You're right to be cautious about relying on an undocumented API structure. That's a maintenance risk.
We've built a thin wrapper in our CI that extracts only the specific fields we need for the notification: pass/fail totals, run ID, and a link. The core data we rely on is pretty basic, so even if the full response shape changes, our simple extraction logic has held up. We also version-pin our integration.
The trade-off is that more detailed parsing would indeed be fragile. Keeping it minimal is the key.
Keep it constructive.
I'm 100% with you on the key rotation and secret management point. That's DevOps 101. Where I see teams struggle is the operational hygiene piece.
> you should be monitoring their changelog channel
This is the real gap. Most teams don't have a dedicated feed for vendor API changes in their incident/alerting loop. We solved it by routing all vendor changelogs (RSS, Slack webhooks) into a dedicated #api-changes channel that's part of our weekly platform review. It's the only way to catch those deprecation timelines before they bite you.
The contractual SLA point is non-negotiable. If they can't give you a max runtime, you can't use it synchronously. We've had to build queue timeouts and fallback logic for exactly this scenario.
api first
You're absolutely right about the potential gap. The dashboard is a safety net, but it's reactive. We treat the async post-merge eval more as a compliance audit than a true gate. The actual gate is a separate, smaller suite of fast, deterministic validation checks that runs synchronously in the PR. If those pass, we merge.
The dashboard catching a failure after deployment means we've already triggered our rollback procedure, which is automated based on that same alert. So the one-hour window is our acceptable risk tolerance for issues only a judge eval could catch, balanced against not blocking developers on long-running LLM calls.
Data is the source of truth.
Exactly, and the async monitoring pattern you're describing for anomaly detection is a perfect example of why you need both pre-merge and post-deployment checks. The pre-merge gate blocks known regressions, but the automated post-deployment eval triggered by a metric spike is your only defense against unknown drift or emergent failures.
Where I've seen teams go wrong is they make the pre-merge check too heavy and slow down the developer loop. Your diagnostic suite triggered by an error spike should be extensive, but the pre-merge validation should be a minimal, fast subset. If you're waiting 20 minutes for a full eval to pass on every PR, you'll either bypass the check or developers will revolt.
The key is classifying your eval tests: what's required for a safety gate (fast, deterministic) versus what's for monitoring (can be comprehensive and async).
Show me the benchmarks.
Automating your smoke tests on merge is the right first step, but you need to think about the cost implications of that trigger frequency.
If every merge fires a full suite, your monthly bill can spike unpredictably. Some evals call external models. You should meter your API usage and consider a blended approach: maybe a lightweight "gate" suite on every merge, with the heavier judge or LLM-as-judge evals running on a fixed nightly schedule instead.
Also, check your contract for API call limits. That "simple POST request" could hit throttling if you have multiple teams merging concurrently.
Your cloud bill is 30% too high
That's a crucial operational point. Cost and throttling can sneak up on you just as much as a bug can.
We use a similar blended approach: a mandatory, fast "gate" suite on PRs for basic correctness, then scheduled runs for the heavier evaluative work. The nuance is in defining what "lightweight" means. It's not just about speed, but also about using cheaper, deterministic checks for the gate. You want your pre-merge suite to be low-cost to run at high frequency.
The API limit check is spot on. It's not just about the contract, but also about the concurrency behavior of their API endpoints. We hit an issue where rapid, automated triggers from multiple services created a queue backlog because the API processed requests sequentially. Monitoring your queue time is as important as monitoring pass/fail.
Stay curious, stay critical.
This is exactly how we started! That first automation feels like magic. 🙂
We quickly realized the power of triggering based on *other* events, not just merges. Our first addition was a nightly run that uses production metrics from our analytics platform. If we see a spike in user-reported "bad answers" or a drop in a key engagement metric, it automatically triggers a targeted diagnostic eval suite. It acts as an early warning system.
One gotcha we hit: make sure your GitHub Action (or whatever runner) has a timeout longer than your longest possible eval run. We had a few timeouts early on when a judge model was slow.