Hey folks, I’ve been absolutely buried in evaluation land for the last half-year, and I wanted to share a pretty significant shift in my workflow that’s paid off big time. For context, I was a pretty heavy user of OpenAI’s Evals framework for a long time—it was my go-to for benchmarking chatbot responses and RAG pipeline outputs. But around six months ago, I decided to port my primary evaluation suite over to Giskard, and the difference in measurable accuracy and, frankly, my own debugging sanity has been night and day.
So, why the move? I hit a wall with scaling and reproducibility. With OpenAI Evals, I was stitching together a lot of custom Python scripts to handle things like:
* Dynamic test case generation based on my knowledge base
* Visualizing drift between model versions
* Automatically generating “adversarial” test cases to find edge cases
Giskard essentially bundles a lot of that philosophy into a single, opinionated framework. The core concept of wrapping your model (LLM or otherwise) and dataset into their objects felt clunky at first, but it creates a unified surface area for applying tests.
Here’s a concrete example of the accuracy gain. I have a RAG pipeline for internal documentation. With my old Evals setup, I was measuring accuracy mostly on a set of ~50 static Q&A pairs. My “accuracy” was a score derived from LLM-as-judge grading on faithfulness and relevance. After migrating to Giskard, I used their `scan` function on my model. It automatically generated over 200 new test cases by perturbing my original questions—things like introducing typos, synonyms, or negations. Running my pipeline against this *expanded* test suite revealed a whole class of failures where the retrieval was brittle to minor rephrasing. Fixing those (mostly through better query transformation and chunking) boosted my *real-world* accuracy on user queries by an estimated 15-20%. The old static suite simply wasn’t finding those holes.
The other massive win is in monitoring. Setting up a “testing as code” regimen where my evaluation suite runs automatically on PRs has been a game-changer. Giskard’s integration with the CI/CD pipeline means I can now get a report showing:
* Performance regression on any core functionality
* New vulnerabilities introduced (e.g., sensitivity to personal identifiable information, even if not in my training data)
* A clear, visual diff of what changed in the outputs
It’s not all sunshine—the learning curve is steeper, and you have to buy into their way of structuring things. But for a tinkerer who loves to compare tools side-by-side, the data doesn’t lie. My evaluation coverage is broader, the tests are more robust, and I’m catching issues much earlier. For anyone else feeling like their eval process is becoming a pile of scripts held together with hope, I’d really recommend giving Giskard a look. It’s turned evaluation from a periodic report card into a live, interactive debugging dashboard.
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?