When we set out to build the "AI Diet Planner", our goal was simple: replace generic, cookie-cutter meal plans with hyper-personalized nutrition guidance that respects dietary restrictions, budget, and local grocery availability.
The Challenge with LLMs in Nutrition
Large Language Models are prone to hallucination. For a creative writing app, hallucination is a feature. For a diet planner, telling someone with a severe peanut allergy to eat a Snickers bar is catastrophic.
Our Architecture
We solved this by using LLMs strictly for reasoning, not for data retrieval. We built a vast, structured SQL database of verified nutritional ingredients. The system works in three phases:
- Intention Parsing: GPT-4 extracts the user's macros, allergies, and goals.
- Constraint Solving: A deterministic backend queries our database to find safe ingredient combinations that hit the macro targets.
- Creative Generation: GPT-4 is handed the verified ingredients and writes appealing recipes.
This hybrid approach guarantees 100% safety and mathematical accuracy while preserving the conversational flexibility that makes AI so powerful.