Stop vibe-checking your model: write real evals with inspect_ai, the UK AI Safety Institute's framework
Originally published at AI Frontier Post Here is the uncomfortable truth about LLM development in 2026: the models got dramatically better, and most teams' measurement discipline did not. A prompt tweak here, a model swap there, a thumbs-up from a product manager — and nobody can reconstruct, a month later, what was actually tried or what it did. Frontier labs don't work this way. They work in…
In 2026, large language model (LLM) development advanced significantly, yet many teams failed to implement robust measurement discipline. To address this issue, the UK AI Safety Institute created the inspect_ai framework, which provides a structured approach to evaluation. This framework emphasizes versioned datasets of test cases, deterministic graders, and logs that ensure every run is comparable to others.
A Task binds together a Dataset (test cases), a Solver (the model or agent that generates responses), and a Scorer (the component that converts answers into numerical scores). The framework includes over 20 model providers, sandboxed tool execution, parallel runs, resumable logs, and a web viewer for reading transcripts. It also features 200+ prebuilt benchmark implementations in the inspect_evals package.
To use inspect_ai, one must have Python 3.10 or later and install the inspect_ai package via pip. The framework was tested using inspect_ai version 0.3.273 on Python 3.12 with CPU-only setup. The tutorial demonstrates how to create a simple evaluation with four multiple-choice questions, a solver that formats them, and a scorer that checks for the correct letter choice.
The example code consists of 20 lines, which are saved in a file named hello_eval.py. When executed, the script runs the evaluation using the mockllm/model provider and reports an accuracy of 0.75. The log generated by inspect_ai includes the full transcript, scores, and metadata for each run, serving as the deliverable rather than a byproduct.
Researchers can open the evaluation log using the inspect view command, which launches a local web UI displaying the results. The tutorial also shows how to create custom graders, such as a numeric scorer for partial credit, and how to compare different evaluations to identify regressions.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
