Why Your Developer Needs Examples of Real Business Work

A description of the ideal process rarely explains the whole business. Real work includes amended orders, incomplete enquiries, absent approvers, and customers whose needs do not fit the usual path. Representative examples help a developer understand these conditions before they become expensive surprises in a product used by employees or customers.

Show the transaction, not only the form

A hypothetical events company asks for a booking system. Its blank form lists dates, guest numbers, and contact details. A completed booking reveals more: several revisions, a deposit received in two payments, a change of contact, and a venue restriction that staff must confirm separately.

Give the developer a small selection of anonymized examples that show those relationships. Explain what each record represents, where it originated, and which information employees consider authoritative. The objective is to communicate the work, not to hand over an uncontrolled archive containing unnecessary personal information.

Include the messages and reports associated with the task. A booking may look complete in a form while still waiting for a supplier's confirmation. The wider account shows where the proposed system must cooperate with people and existing tools instead of assuming that all activity occurs on one screen.

Choose examples that challenge assumptions

Select a normal case, a changed case, and a case that could not proceed. Explain why each matters. For the events company, a cancellation before the deposit and a cancellation after suppliers have been committed have different consequences. Calling both “cancelled” without further explanation can hide important business rules.

The Government Digital Service's discovery guidance recommends understanding the wider context and constraints before building. In a commercial project, representative examples make that context concrete and help distinguish a common task from a significant exception.

Do not seek every possible anomaly at the outset. Prioritize by frequency, consequence, and the difficulty of resolving the case manually. Mark uncertain rules clearly. A developer needs to know when an example demonstrates an agreed policy and when it demonstrates a problem the company has not yet resolved.

Explain decisions that are invisible in records

Experienced employees often infer meaning from a conversation, a customer relationship, or the timing of a request. Ask them to explain what they knew when they acted. If that knowledge is necessary, consider whether the system should record it, request a decision, or leave the task with a person.

In the hypothetical events company, an employee may recognize that an enquiry is exploratory rather than a confirmed reservation. The software cannot reliably reproduce that distinction unless the business explains how it is established. A clear status definition may be needed before implementation begins.

Identify who can override the usual rule and what should be retained afterward. The owner should settle commercial authority; the developer should recommend how the product applies it consistently. Otherwise software can turn an employee's informal assumption into a rule that affects every future transaction.

Use examples during review

Return to the representative cases when evaluating early work. Ask employees to perform the task with realistic information, observe their questions, and compare the result with the agreed outcome. This creates a traceable link between the business problem and the delivered behavior.

The Government Data Quality Framework distinguishes completeness from accuracy. That distinction matters in review: a fully populated booking may still contain incorrect dates or an outdated contact. Checks should reflect the information the business needs to act correctly.

Explain when test examples are simulated and avoid using their success as proof of market demand or operational performance at scale. They demonstrate particular behavior under stated conditions. Broader confidence requires additional evidence from introduction and ongoing use.

Make customization more precise

Holism combines AI-assisted development, a Software Product Line foundation of reusable capabilities, and a human developer. Concrete cases help the developer identify which established capabilities fit and which rules require adaptation. AI supports exploration and implementation; human judgment determines how those choices should serve the operation.

For an owner, this avoids the need to express every requirement as a technical instruction. The owner and employees supply domain knowledge, while engineering decisions remain with a professional. The reusable foundation is valuable when customization preserves consistent information and rules rather than creating isolated responses to individual requests.

Examples of real work reduce ambiguity when they show the original situation, decision, outcome, and remaining uncertainty. They are most useful as a continuing reference for development and review, giving the business a concrete basis for assessing whether the product understands its operation.