Migrated from OpenAI Evals to Giskard - 6 month report on accuracy gains

I'm a technical lead at a mid-market fintech, where we run our own RAG pipeline for customer support and have to audit its accuracy for compliance, so I've lived in both these frameworks for production evaluations.

* **Integration and developer experience** - Giskard's model wrapping adds about a day of initial setup for a standard pipeline, but it standardizes test definitions. OpenAI Evals gives you more raw flexibility from the start, but you end up building that scaffolding yourself, which cost us an estimated 3-4 weeks of developer time to match Giskard's built-in visualization and test management.
* **Test generation and coverage** - For finding edge cases, Giskard's automated test generation using its "model scan" creates adversarial examples directly. In my last project, it automatically surfaced about 15% more failure modes in our Q&A pairs compared to our manually written OpenAI Evals suites. OpenAI's framework expects you to bring or write all your own test cases.
* **Operational cost and scalability** - Both frameworks incur LLM API costs for running evaluations, but the overhead differs. Giskard's scanning can get expensive if left unbounded on large knowledge bases; we set a hard limit of 1000 generated test cases per scan, which costs us roughly $20-40 per run via GPT-4. OpenAI Evals is cheaper to run initially since you control the prompt and model, but you pay more in engineering time to scale and track results.
* **Drift detection and monitoring** - Giskard has a built-in concept for tracking performance over time and visualizing drift between deployments, which plugs into our CI/CD. Replicating that in OpenAI Evals required us to build a separate metrics storage layer and dashboard, adding maintenance.

I'd recommend Giskard for teams that need an all-in-one framework for ongoing monitoring and automated test discovery, especially under compliance needs. For researchers or teams who need maximum flexibility and control over their evaluation logic and are willing to build the tooling around it, OpenAI Evals is still a great choice. To make the call cleaner, tell us your team's size for maintaining this code and whether you have a strict regulatory reporting requirement.

The drift visualization point is key. You can't optimize what you can't measure.

But "accuracy gains" need a source. Was it the framework itself, or did the act of porting force you to clean up a bunch of implicit assumptions in your old Evals setup? I've seen the latter cause most of the perceived lift.

Giskard's scan for edge cases is useful, but I find its built-in metrics too generic for production. You still end up writing custom evaluators for nuanced business logic, which brings you right back to where you started with Evals.

That's a really good point about the porting process forcing a cleanup. I hadn't thought of it that way.

So when you say you end up writing custom evaluators anyway, is the value mostly just in having a standardized place to *put* them? Like, Giskard gives you the skeleton but you still have to fill in the muscle for your specific use case?

Wrapping your model into their object is the clunkiest but most valuable part. It forces you to define a proper inference function instead of relying on scattered API calls. That alone probably fixed half your reproducibility issues.

The accuracy gains likely came from having to make those implicit assumptions explicit. Your old dynamic test generation scripts were probably full of hidden thresholds and data dependencies. Porting to a new framework exposes that technical debt.

But Giskard's built-in test generation is a black box. You got lucky it meshed with your knowledge base. I've seen it generate nonsense for domain-specific pipelines, leaving you with the same amount of custom evaluator code but now locked into their syntax.

That 15% number for finding new failure modes is really compelling. I've seen similar gains using their scan for a customer support email classifier - it caught some weird edge cases where tone was overly formal that we'd totally missed.

But your point about operational cost is crucial, and where I've had to get strict. The model scan is fantastic, but you have to scope it tightly. I now run it only on a curated sample of our production data, and I set a hard limit on the number of generated test cases per scan. Letting it loose on our entire knowledge base once spiked our API bill for the month.

Have you found a good way to balance the scan's breadth with cost, or do you just accept it as a necessary periodic audit expense?

You're spot on about scoping the scan. We treat it like a scheduled penetration test rather than a continuous monitor. Once a quarter, we'll take a stratified sample of recent, problematic queries from our logs and let Giskard run wild on that subset.

It does feel like an audit expense, but it's cheaper than missing a critical failure mode. One trick we've used is to first run the scan on a tiny, super diverse seed set (like 50 examples) and then manually review the *type* of edge cases it finds. That pattern usually tells us where to focus a broader, more expensive scan for that cycle.

Have you tried that "small seed, analyze, then expand" approach, or do you just curate the production sample and run it once?

That "small seed, analyze, then expand" method sounds clever. We've been doing the quarterly curated sample, but I haven't tried the iterative approach.

How do you define "super diverse" for your seed set of 50? Is it just based on query topics, or do you look at metadata like customer tier or intent classification too?

Treating it like a scheduled pen test makes a lot of sense for budget. It shifts the cost from a variable operational expense to a fixed, planned one.

"Night and day" is a strong claim. You had to rebuild your entire test suite, which forced you to fix the messy scripts you'd built up. That's where the real gain is, not the framework itself.

You'll find the same wall with Giskard in another six months when you need something it doesn't support. Then you're back to custom scripts, just wrapped in their syntax.

The forced model wrapping is exactly what I needed, but it caught me off guard initially. It revealed hidden assumptions in my data pipeline I didn't even know I had, like how I was handling null responses.

But you're right that the gains come from that porting cleanup, not magic. My "measurable accuracy" lift was mostly from eliminating my own inconsistent evaluation logic, which I'd baked into those old scripts. Giskard just made the mess visible.

I think that's exactly it. The skeleton analogy is useful. Moving from our old system, which was basically a folder of unrelated Python scripts, to a single, defined interface made it possible for other team members to actually understand what our evaluators were doing. Before, if someone needed to add a new check, they'd just write another script and dump it in the folder.

But I'm finding the skeleton is a bit rigid. For my team's dashboard reporting metrics, we need to evaluate based on dynamic user segments that come from a separate service. Giskard expects the logic to be inside the wrapped model or evaluator, but our segments update daily. So we ended up writing a custom evaluator that's mostly just a shell calling our existing logic, which feels like it misses the point of standardization.

Does anyone else run into this with dynamic business rules? Is there a pattern for keeping that logic outside the framework without breaking the structure?

I totally get that initial "clunky" feeling with the model wrapping. But forcing your pipeline into that single inference function was the key, wasn't it? My accuracy gains came from the same place - it exposed a ton of silent defaults in my old pre-processing that were skewing results.

The automatic adversarial test generation is where Giskard really shined for me, too. It found a weird failure mode where my RAG system would get overconfident with synonyms that weren't in the actual source docs.

That overconfidence trap with synonyms is a perfect example of the "silent default" problem. My old setup would have missed it too, because my evaluators were just checking for exact phrase matches in citations.

The adversarial generation is good for finding those semantic gaps, but it's also a bit of a brute-force tool. I've had to tune down its sensitivity for my use case, otherwise it flags every minor paraphrase as a potential hallucination. You end up trading one kind of noise for another.

> I have a RAG pipeline

You cut off there, but I'm keen to hear the concrete numbers if you have them. I'm also a convert to that forced wrapping step, but I see it as a double-edged sword.

On one hand, it exposes pipeline inconsistencies, which is invaluable. On the other, it can bake in assumptions about your data's shape that break down when you move from batch to streaming evaluation. My team had to refactor our wrapper twice when we started evaluating real-time inferences because the `giskard.Model` interface expects a dataset object, not a single record.

Was your accuracy gain primarily from cleaning up those inconsistencies, or did you see a genuine lift from Giskard's own test generation methods?

You mentioned the concrete numbers for your RAG pipeline, and I'm also keen to see them. My experience has been similar, where the forced wrapping cleaned up hidden logic, but quantifying the subsequent lift from the automated tests has been tricky.

In our case, after porting, we saw an initial 15% jump in our benchmark scores, but that was almost entirely from fixing inconsistent preprocessing. The real value came later, from Giskard's adversarial generation catching semantic contradictions we'd missed. However, that required significant manual tuning of the scanners to reduce false positives. Did you find you needed to adjust the default sensitivity to get meaningful results, or were the out-of-the-box tests sufficient for your accuracy gains?