AI ยท Feature
Public benchmarks make it easier to compare models, but do not answer every question arising in your own day-to-day work. A manageable, carefully assembled test set can reveal which errors actually matter. Clear criteria, difficult counterexamples and a willingness to assess refusals to answer, clarification questions and the human effort needed for corrections as part of quality are all important.
The task determines the standard
An assistant for internal documents needs different capabilities from a creative writing partner. Evaluation should therefore begin with a description of the actual purpose. What information may the system use, what output format is required, and which errors would have particularly serious consequences? The NIST risk profile for generative AI provides a framework for considering risks in the context of use. Open projects such as OLMo also make it easier to examine development and evaluation artefacts. Nevertheless, testing of your own remains necessary. A general benchmark score cannot establish whether a particular document collection is being accessed and interpreted correctly or an internal category reliably recognised. A small test using real, cleaned-up examples is often more informative than a large number whose underlying tasks have little connection to the planned use.
Deliberately include failure cases in the test
A test set should not contain only easy questions with clear answers. Missing information, contradictory records, unusual wording and outdated documents are equally important. These reveal whether the system responsibly asks for clarification or fills gaps with invented details. Criteria are defined in advance for each task: factual accuracy, reference to sources, completeness and compliance with the format can be assessed separately. Open-ended texts require transparent assessment rules and, where possible, more than one reviewer. Nor should the test set be continually used to adjust prompts without holding back independent control cases. Otherwise, the system may improve only on familiar examples. A good test examines generalisability and behaviour at the boundaries, not merely the ability to solve familiar tasks again.
Quality also includes operation
Alongside correct answers, response time, costs, data protection and the effort required for subsequent editing all matter. A model with a slightly higher accuracy rate may be worse in practice if its errors are harder to spot. Likewise, an honest clarification question can be more useful than an immediate but unsupported answer. Results should therefore be broken down by error type and measured again after every significant change. These include model updates, new search methods and changes to document collections. A short, versioned test report records what was tested and what limitations remain. This produces not a final certification, but a repeatable learning process. The central question is not which model wins overall, but which system performs the desired task reliably enough under known conditions and behaves appropriately when uncertain.
Sources & further reading
- NIST: Generative Artificial Intelligence Profile (2024)doi.org
- Ai2: OLMoallenai.org
Written in our own words with AI assistance, based on the sources listed above. The text distinguishes research findings, technical prototypes and established knowledge. This feature is not a breaking-news report.