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Meal Review

Review Meal Generation using AI

The current system generates meals with incorrect recipes, images, and nutritional information. This leads to user dissatisfaction and potential health risks due to inaccurate dietary information.

Objectives​

  • Ensure that all generated meals are accurate and reliable.
  • Reduce manual verification effort over time by improving the meal generation process.
  • Achieve a 99% accuracy rate in meal generation, minimizing errors and enhancing user trust.

Benefits​

  • Increased user satisfaction and trust in the meal planning service.
  • Reduction in manual verification efforts, leading to cost savings.
  • Compliance with dietary guidelines and user-specific dietary needs.

Functional Requirements​

Meal Generation​

  • Send generated meals to the UI for initial verification.
  • Utilize a meal engine to improve the accuracy of generated meals over time.

Image Verification​

  • Manually review and verify the images associated with each meal.
  • Ensure images accurately represent the meal recipes and are of high quality.

Nutrition Analysis​

  • Validate the nutritional information of each meal against standard databases.
  • Ensure nutritional data aligns with dietary guidelines and user preferences.

Meal Review​

  • Validate meal recipes to ensure they meet specified requirements.
  • Verify recipes are feasible within the stated cooking time and budget constraints.
  • Ensure meals align with dietary restrictions and preferences, including seasonal adjustments and allergies.

Support Team Validation​

  • The support team reviews the updated meal plans.
  • Provide feedback or request adjustments if necessary.
  • Regenerate meals if they do not meet the satisfaction criteria.

Internal Audit and Validation​

  • Regularly audit meal plans for accuracy and completeness.
  • Maintain records of audits and validations for future reference.

Solution Overview​

To address the issue of incorrect meal generation, the proposed solution involves a combination of automated and manual verification processes, supported by a robust meal engine that improves accuracy over time.

Solution Components​

Meal Generation Engine​

  • An advanced meal generation engine will be developed to create meal plans based on predefined criteria.
  • This engine will send the generated meals to the UI for initial display and verification.

Manual Verification​

  • Support staff will manually verify images, recipes, and nutritional information.
  • This process ensures that each meal meets quality standards before finalization.

Automated Validation​

  • Automated checks against nutritional databases and dietary guidelines will validate the nutritional information of meals.
  • Regular audits and validations will be conducted to maintain accuracy.
  • If a meal has already been verified, the automation tool will not verify it again.

Feedback Loop​

  • A feedback loop will be established where support staff can provide input on meal accuracy.
  • The meal generation engine will learn from this feedback to improve future meal plans.

Discord Channel Updates​

  • Finalized meal plans will be updated on the Discord channel.
  • Detailed information for each meal, such as ingredients, recipes, preparation steps, cooking times, servings, and sources, will be included.

Conclusion​

By implementing this solution, we can ensure that the meals generated are accurate and reliable, thereby enhancing user satisfaction and trust. The combination of manual and automated processes will help achieve the desired accuracy rate, while the feedback loop will ensure continuous improvement.