The most impressive answer in a demo is not necessarily the most useful draft in your workflow. A writing tool can sound fluent while adding a claim your source never made, missing a qualification or taking longer to edit than an ordinary first draft.
To evaluate AI writing tools, test the work you actually need to publish. Our recommendation is to compare cost per usable, reviewed output rather than price per subscription alone. This article provides a practical test brief, scoring method and fictional cost example. It is a buying framework, not a hands-on product ranking or a claim that we tested every vendor.
Choose the job before choosing the model
A tool that helps brainstorm campaign angles may be unsuitable for summarising a technical announcement. A system designed for short adverts may need substantial additional controls for long-form reporting. Decide what success means for one workflow first.
Write a brief describing the audience, output format, sources, factual boundaries, tone and approval process. Include what the tool must not do: invent customer results, turn a proposal into a confirmed launch, or attribute an opinion to someone who did not express it.
If the underlying brief is vague, better prose can conceal the problem. The brand asset audit offers a related discipline: define the brand’s actual cues and claims before expecting a tool to reproduce them consistently.
Prepare a small but revealing test set
Use several representative tasks with material you have permission to process. Include a straightforward assignment, a long source, conflicting source statements, missing information, an old announcement and a brief that requires the model to say it does not know.
For a news-style test, supply a short source pack containing a release date, a limited rollout and a stated eligibility condition. Ask for a headline, excerpt, article and source notes. The important test is whether those conditions survive into the output, not whether the headline sounds exciting.
Repeat the assignments because a single successful answer may not represent normal behaviour. Keep the supplied material and instructions the same between tools, and record model name, available settings and test date. Do not enter private client information merely to make the test feel realistic; anonymised or purpose-built examples can test the same requirements.
Anthropic’s discussion of evaluations describes testing defined tasks against success criteria and using repeated trials because outputs can vary. Our editorial adaptation is simple: write the acceptance rules before seeing which vendor produces the most appealing prose.
Use hard failures before subjective scores
Some errors should stop publication regardless of writing quality. An invented quote, unsupported result, incorrect eligibility statement or exposure of confidential material is not balanced out by a good introduction. Treat those as failures that require investigation.
Then score the outputs that survive on practical dimensions: factual support, preservation of important qualifications, useful organisation, clarity for the intended reader and the amount of editing required. Use short written definitions for each rating so reviewers are not scoring different things.
For example, “source support” can mean that every material factual claim can be traced to the supplied evidence or a separately verified source. “Useful analysis” can mean an explicitly labelled implication with a reason and a boundary, rather than an unsupported prediction about the entire industry.
Try this prompt as a starting point
“Using only the supplied source pack for factual claims, write an article for [audience] about [topic]. Preserve the dates, eligibility limits and uncertainty. Separate confirmed information from analysis. Explain one practical implication and one situation where it would not apply. Do not invent statistics, quotations, personal experience or product testing. Return a title, excerpt, structured article and a list of claims the editor should verify. If the evidence is insufficient, identify the missing information instead of completing it from guesswork.”
This is a test prompt, not a guarantee of factual accuracy. Open the cited sources and check the claims yourself. A model-generated verification list is a useful navigation aid; it is not independent confirmation of its own output.
The NIST generative AI profile identifies confidently presented false content as a risk. Fluency and apparent certainty should therefore not serve as your acceptance test.
