Recommended Free Tools
To replace a vibe check with a real evaluation, define the task, examples, solving procedure and scoring rule before you run the model. Inspect AI’s Python package, inspect_ai, organizes those pieces as a task built from a dataset, a solver and a scorer. The resulting score tells you how the model performed on that chosen setup—not whether it is good at everything.
How do I write real evals with Inspect AI instead of vibe-checking my model?
Start with a specific claim you want to test, such as whether a model returns a valid answer in a constrained format or follows a particular instruction. Then create examples that represent that claim and decide in advance what counts as a passing answer. Inspect is described by the UK AI Safety Institute and Meridian Labs as “a framework for frontier AI evaluations developed by the UK AI Safety Institute and Meridian Labs.” Its task model makes the parts of an evaluation explicit and reusable. See the Inspect overview.
As an Amazon Associate I earn from qualifying purchases.
A minimal Inspect task combines a dataset, a solver and a scorer, and is returned from a function decorated with @task. The dataset supplies samples; the solver produces or elicits model responses; the scorer judges those responses against targets or grading criteria. That separation is useful: a score is meaningful only in relation to the samples, solving procedure and scoring rule that produced it. The Tasks documentation describes this structure.
Define the claim and dataset before choosing a score
Turn a broad goal into a testable behavior
“Is this model good at support?” is too broad to score consistently. Narrow it to a behavior—for example, whether responses include a required field, correctly identify a supplied fact, or meet a stated criterion. This is a design choice, not a guarantee that the resulting dataset represents every real support interaction.
#1 Best Overall
Make each sample interpretable
For each example, provide the input and an appropriate target or grading criterion. If the answer should be exact or constrained, make that constraint clear. If judgment requires context, write down what a satisfactory answer must do. Inspect’s dataset is an explicit task component; unclear examples or targets make the final metric harder to interpret.
Choose a solver that matches the interaction
The solver is the part of the task that produces the model response. Configure it to reflect the interaction you actually want to evaluate, rather than treating the model’s answer as independent of how it was prompted or asked to work. Inspect allows a task’s solver to be replaced for experiments, so a task can be reused to compare solver variants. Keep other choices fixed when you want to understand the effect of changing the solver. The task documentation and components documentation cover reusable components.
Match the scorer to the claim
A scorer answers a different question from the solver: it judges the response. Inspect provides direct matching, model-graded scoring and custom rubrics. Choose based on what your evaluation claims to measure, because the scoring method determines what a passing result means. The Scorers and Scoring documentation describe the available approaches and workflow.
Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWindows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall- Exact or substring matching: can suit constrained answers where the expected text or a recognizable part of it is the criterion.
- Model-graded scoring: can be used when evaluating an answer requires judgment beyond a direct match.
- A custom rubric: lets you state criteria tailored to the claim, particularly when open-ended responses need structured judgment.
These are design examples, not universal prescriptions. A model grader or rubric introduces judgment into measurement; do not present its output as an unquestionable ground truth. Inspect’s scoring documentation explains scoring as a distinct part of the evaluation.
Keep model errors separate from measurement and run failures
A wrong answer, an execution failure and a grader or instrumentation failure are not interchangeable. If a run cannot produce a usable response, or the grading step fails, silently counting that event as a model failure—or as a success—can distort the metric and its denominator. Decide how such cases should be represented and reported, and preserve the distinction in the results. Inspect’s Scoring Policy addresses these outcome and denominator distinctions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run, inspect and refine the evaluation
After a run, examine what the score summarizes: the selected samples, solver and scorer. If the result is surprising, inspect examples and grading outcomes rather than treating one aggregate number as a complete verdict on the model. Then change one evaluation choice at a time where possible.
Inspect supports re-scoring stored logs with a different scorer, which can help isolate a scoring change from a new generation run. It also supports experiments with alternate solvers. These capabilities make evaluation design iterative: refine examples, solving procedure or grading criteria, then be clear about which changed between results. See the Scoring Workflow and Components pages.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesQuick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
