A longer application list does not replace proof of work
This article uses a typical job-search scenario rather than a claim about a specific person. The candidate plans to submit thirty applications but has only job descriptions, course notes, and a general statement that they can use AI. A hiring manager still cannot inspect how the candidate defines a task, checks an output, or handles a failure.
One small work sample can answer those questions better than another page of tool names. Choose a task that appears repeatedly in target roles, such as turning support notes into an action list or converting a rough brief into a structured report. The sample should show the input boundary, the expected format, the result, and the candidate's own corrections.
Sanitize the material before testing any model
Do not use a real customer's spreadsheet, an unredacted resume, or an internal company document simply because it looks more convincing. Copy the material first and replace names, phone numbers, email addresses, company names, customer identifiers, and other unnecessary details with clear placeholders. Preserve the task structure, not the identity of the people in it.
The sanitized version should be understandable on its own. Add a short note that explains which fields were replaced and why. This gives the work sample a safety boundary and helps a reviewer understand that responsible data handling is part of the delivery, not an afterthought added after the model has already received private information.
Write acceptance criteria before the first real call
Define the output before comparing models. A useful acceptance checklist might require five action items, a fixed table format, no invented facts, and a short explanation for every uncertain statement. Without this checklist, each output will be judged by a different feeling, and the candidate may keep changing the prompt until one result happens to look impressive.
Use one sanitized input and the same criteria for a small shortlist. Check the current model and channel status, then make one real request through the correct interface. A model being visible in a marketplace does not prove that the requested task or endpoint works. The smallest real call is the first operational evidence.
Use an isolated key and preserve the cost record
Create a separate, revocable key for the work-sample project instead of reusing a shared credential. Record the request count, failures, retries, final usage, and the minutes spent correcting the result. These records make the sample reproducible and show whether the apparent quality depends on expensive repetition or hidden manual rewriting.
Within the models currently and lawfully offered at https://APIToken.Company, the site can serve as an entry point for checking current models, public status, isolated keys, usage records, and small real calls. It does not guarantee employment, permanent availability, or a production-ready result. Current prices, groups, models, and availability follow the live pages and the correct endpoint test.
Package the result so another person can inspect it
The final sample should contain the sanitized input, the acceptance checklist, the chosen output, a short change log, and a compact usage record. Explain what failed, what you changed manually, and where you would stop if the same error repeated or the editing time exceeded the value of the task. A flawless-looking screenshot without this trail proves much less.
The smallest action today is to copy one representative task and replace every personal or company identifier. Tomorrow's work can be the acceptance checklist and one real call. This staged approach protects privacy and budget while producing evidence of delivery. External views, applications, interviews, registrations, API calls, and paid outcomes remain unverified unless separate evidence exists.