Preparing A Tiny Model Intake Checklist Before Quote Work
A practical owner guide to collecting the right task details, examples, limits, and review needs before asking for a focused local model quote.
Why the intake checklist matters
A tiny model quote should not begin with model size. It should begin with the work the owner wants to improve. When that work is described clearly, the builder can tell whether a focused local model is a good fit, what data is needed, how much review the first version requires, and what kind of delivery packet will make the result useful.
Without an intake checklist, tiny model projects can sound simple while hiding important details. An owner may ask for a model that handles support, sales, documents, and private notes in one request. That may be a real business need, but it is not one clean first model. A checklist turns the conversation into practical scoping. It helps everyone agree on the first narrow lane before time is spent collecting examples or planning runtime.
The goal is not to make the owner fill out a complicated technical form. The goal is to gather enough plain language context that a quote can be honest. A good quote should explain the job, the likely data work, the expected review process, the launch boundaries, and the support needed after delivery.
Name the one job first
The first item on the checklist should be the one job sentence. The model reads this kind of input and returns this kind of output. That sentence keeps the project grounded.
Useful examples sound like this. The model reads incoming service messages and returns category, urgency, short summary, and review flag. The model reads a rough product note and returns a clean title, key details, missing fields, and owner review status. The model reads a lead form and returns fit level, missing information, and suggested next step.
If the sentence includes too many verbs, the first quote should probably be split into phases. Read, classify, summarize, rewrite, score, extract, and answer are different behaviors. A tiny model can support more than one behavior over time, but the first version is easier to test when it owns one repeated job.
Describe the real input source
Next, record where the input comes from. This may be a form, inbox, chat transcript, spreadsheet, call note, product record, ticket system, document folder, or private owner note. The source matters because it tells the builder how messy the work will be and how much preparation is needed.
The checklist should ask for a few ordinary examples, not polished demo examples. If customers often write short messages, include short messages. If staff members mix English and Spanish, include that pattern. If records arrive with missing details, include missing details. The quote should be based on the real shape of work, not on perfect samples that the model will rarely see after launch.
Private details should be removed before examples are shared for quoting. Names, phone numbers, addresses, account numbers, medical details, candidate identifiers, and sensitive business notes can be replaced with safe placeholders. The structure of the example is useful. The private identity behind it usually is not needed for early scoping.
Define the output people will act on
A focused model should produce an output that helps a person or system take the next step. The checklist should ask what fields are required, what format is preferred, and who uses the result.
For many Tiny Model Generator projects, the first output is simple JSON or a short structured note. The exact fields should come from the business action. If a lead will be reviewed by an owner, the output may need fit level, missing details, reason, and next step. If a support message will move to a queue, the output may need category, urgency, summary, and review flag. If a document will be checked by staff, the output may need extracted fields plus a confidence note.
Avoid asking for fields that do not change any action. Extra fields make examples harder, testing slower, and results less trustworthy. A smaller output can be stronger because the owner can review it consistently.
Mark the boundary cases early
The intake checklist should ask what the model must not decide. This is where a quote becomes safer. A tiny model may prepare a suggestion, but it should not be trusted with every final action on day one.
Boundary cases may include legal advice, medical judgment, hiring rejection, refund approval, pricing promises, customer commitments, private record changes, or any answer that should depend on a manager. The checklist should also ask what the model should do when information is missing. A good fallback may be needs human review, missing timeline, unclear category, outside scope, or ask for more information.
These boundaries affect the quote because they change the testing plan. A model with strong review gates may launch sooner as a helper. A model expected to move work automatically needs more examples, more edge case tests, and clearer rollback instructions.
Count the first week of use
A practical quote should know how often the model will run. The checklist should ask for normal day volume, busy day volume, and the speed that would feel acceptable. Ten items per day is different from five hundred items after a campaign. A response that can wait thirty seconds is different from a response that must appear while a customer is watching.
This estimate does not need to be perfect. It needs to be honest enough to choose a first runtime plan. A small local model may run well on ordinary hardware when the task is narrow, the prompt is short, and the output is compact. If the work has bursts, the launch plan may need a queue, a review screen, or a rule that sends urgent cases to a person first.
Ask what proof will make the owner comfortable
The final checklist item is acceptance evidence. Before a quote is approved, the owner should say what proof would make the first version feel usable. That proof may be a sample evaluation report, twenty held back examples, a one page model card, a short runbook, or a side by side review of model output and corrected answer.
This prevents the project from ending with a demo that feels good but cannot be repeated. A useful tiny model delivery should include the model, the examples used to judge it, the known limits, the owner review rules, and the next improvement path. The intake checklist makes those expectations visible before the quote is written.
A simple intake summary
A strong first quote can be built from seven answers. What is the one job. Where does the input come from. What should the output contain. What examples are available. What should the model refuse or send to review. How often will it run. What proof will make the owner comfortable.
Those answers keep the project small enough to deliver and clear enough to improve. Tiny Model Generator works best when the first model is not sold as magic. It is scoped as a focused operating tool with examples, boundaries, review habits, and a calm path from quote to launch.