Turn business goals into testable requirements

Learning objective

Translate business use cases into testable GenAI functional and nonfunctional requirements. Define what a production solution must accomplish before choosing its model or architecture.

Core idea

A request to build an assistant is a proposed interface, not a requirement. You need a business outcome, a bounded task, and evidence that the task succeeds. A fluent answer can fail all three: it might be unsupported, arrive too late, or require more review than the original workflow.

Separate functional requirements, which describe behavior, from nonfunctional requirements, which constrain the conditions under which that behavior is acceptable. Then connect both to acceptance tests. For GenAI, acceptance includes appropriate clarification, abstention, and escalation. Success does not always mean generating an answer.

The AWS Generative AI Lens places business relevance, success measures, feasibility, and risk assessment in the scoping phase. Use that sequence to avoid turning a service choice into the business case. The output of scoping is an agreement that an experiment can test, rather than a promise that a model will be accurate.

How it works

Bound the business task

Identify the person doing the work, the input they have, the decision they need, and the consequences of a mistake. “Help support staff” is too broad. “Draft a reply to a product-return request from approved policy and the supplied order record” defines an actor, evidence, and output.

Describe the existing workflow as the baseline. Record handling time, review effort, error frequency, and the proportion of requests that need specialists. This makes it possible to distinguish a better-looking interface from a better business outcome. A draft that takes less time to generate but longer to verify can reduce productivity.

Define scope boundaries explicitly. Does the assistant draft a recommendation, communicate it to the customer, or execute a refund? Those are different levels of authority. State which languages, product families, request types, and document versions belong in the first release. Include cases that remain in the existing workflow.

Specify observable behavior

Write requirements around externally observable results. The system must accept the required inputs, identify missing information, use permitted evidence, produce the required response shape, and handle cases outside its scope. Avoid specifying private model reasoning as the proof of success; evaluate the resulting answer and actions.

For each requirement, record a test input, an expected outcome, the scoring rule, and the person who owns the decision. A factual-answer requirement needs reviewed evidence. A formatting requirement needs a validator. A task-completion requirement needs confirmation from the system where the action occurs.

Include negative cases in the contract. Missing evidence should produce a defined fallback. Conflicting policy versions should trigger clarification or escalation. A request from someone without access should not disclose the restricted content. A timeout should leave the request in a known state instead of encouraging an unsafe duplicate action.

Make quality and operating conditions measurable

Quality requirements need a denominator and a rubric. “Accurate answers” could mean factual claims supported by evidence, correct calculations, useful completeness, or faithful summarization. Define these separately so a strong average cannot conceal failure on the consequential part of an answer.

Specify the evaluation population as well as the threshold. Include ordinary requests, difficult requests, missing-information cases, and high-risk slices. A result from short English examples does not establish quality on lengthy multilingual conversations. Repeated trials help reveal variable outputs that a single successful run hides.

Operational constraints describe the envelope in which quality must hold. These include end-to-end latency, concurrent demand, delivery deadlines, data-processing boundaries, retention, recovery, maintainability, and cost per completed task. Specify whether latency means first visible output or a complete usable result. Streaming can improve the first measurement while leaving the second unchanged.

The following example requirements belong to a hypothetical support workflow; their thresholds are not AWS defaults:

Amazon Bedrock evaluations supports automatic and human-based assessment, including custom prompt datasets. That provides a measurement mechanism, but you still define whether the measured output satisfies the business task. Evaluation design is developed further in 5.1; this lesson owns the acceptance contract.

Key distinctions and trade-offs

Business measure versus model measure: Lower handling time is a business result. Answer relevance is a diagnostic signal. Improving the signal matters only if it improves the workflow without violating another constraint.

Hard constraint versus optimization target: A residency rule or authorization boundary eliminates unsuitable options. Latency and cost can be optimized among the options that remain. Do not average a hard constraint into an overall score.

Quality target versus guarantee: A measured acceptance rate describes a tested population. It does not guarantee that every future output is correct. Consequential decisions still need controls that operate per request.

More scope increases potential value but also expands the evidence and evaluation burden. A narrow, well-defined task is easier to validate than an assistant expected to handle every exception. Select scope based on the business decision you need to make, not the breadth of a demonstration.

Architecture / decision scenario

A retailer proposes an assistant that “automates returns.” Interviews show that staff spend most of their time finding the policy applicable to an order. Refund execution already follows a controlled process, and incorrect eligibility advice creates customer disputes.

You redefine the first release as drafting a cited policy explanation for staff review. The contract requires an applicable policy version, an authorized order record, and an escalation when either is missing. The assistant cannot issue refunds. The pilot measures review time and supported-claim quality separately, including old orders and conflicting policy updates.

If drafting improves but review effort rises, the business gate fails. If the most common requests improve while disputed orders fail, you can retain the narrow draft scope and escalate that slice. The requirements make this decision visible before broader automation adds risk.

Common exam traps

• Treating “use an LLM” as the business requirement.
• Accepting answer fluency as evidence of factual correctness.
• Measuring only model latency when the requirement is end-to-end completion.
• Testing successful answers without missing-evidence or unauthorized cases.
• Combining hard constraints and preference scores into one average.
• Omitting human review and failure handling from the cost of the workflow.

What to remember

• Connect business outcomes to bounded tasks and acceptance evidence.
• Define required behavior and operating constraints separately.
• Make clarification, abstention, and escalation valid outcomes.
• Measure representative slices and the complete workflow.
• Resolve hard constraints before optimizing model or service choices.

Knowledge check

References

• AWS, Generative AI lifecycle
• AWS, Evaluate the performance of Amazon Bedrock resources