CASE STUDYConsumer product · AI application

AI-powered meal planning product

Generating a week of meals is straightforward to demonstrate and much harder to make dependable. Each plan has to satisfy nutrition targets, dietary restrictions, budget and available ingredients before it can be shown.

SECTOR: Consumer product
ROLE: Sole engineer

Week planPLAN VALIDATED

Protein targetWITHIN RANGE

Allergen rulesPASSED

Weekly budgetWITHIN LIMIT

Conceptual visual based on the project's engineering approach.

PROBLEM

Meal-plan generation combines flexible suggestions with requirements that are not negotiable. Dietary restrictions, nutrition targets, budget and available ingredients all have to be respected at the same time.

Free-form model output is not sufficient for that job. The product needs a predictable structure and a controlled path when a generated plan fails a rule.

SOLUTION

The generation step produces structured data. Deterministic checks then validate the plan against the hard constraints before the application can display it.

01Requirements enteredTargets, restrictions and available ingredients
02Plan generatedStructured output rather than free text
03Rules checkedHard constraints validated before display
04Plan returnedOnly after the checks pass

● CHECKS PASS

Valid plan shownThe product receives structured data that has passed the required checks.

● REVIEW PATH

Invalid plan held backA failed constraint triggers the repair path instead of reaching the interface.

KEY CAPABILITIES

Structured output

Model output is constrained to a validated schema.

Constraint checking

Nutrition, dietary and budget rules are checked before display.

Controlled repair

Invalid plans follow a repair path instead of reaching the product unchanged.

Evaluation

Repeatable cases make quality changes visible during development.

ENGINEERING APPROACH

Reliability comes from the checks around generation.

The public case study focuses on the confirmed engineering techniques and does not claim unreleased product, user or commercial results.

Schema validation

Outputs are parsed and validated before use.

Deterministic rules

Hard product constraints sit outside model judgement.

Repair path

Failures can be corrected before a plan reaches the interface.

Evaluation cases

Repeatable examples expose quality regressions during development.

OUTCOME

The engineering result is a generation flow that treats model output as untrusted until its structure and hard constraints have been checked.