Design personalized restaurant search and recommendations

Scenario

You are designing a DoorDash-like personalized restaurant recommendation system.

A user types a free-text query (e.g., “spicy ramen under $20”, “healthy vegetarian lunch”, “best burgers near me”). The system should return a ranked list of restaurants (optionally with dishes) personalized to the user.

What to design

1. End-to-end architecture for query → ranked restaurants.
2. How you would incorporate LLMs into the system (where they help, where they shouldn’t be in the critical path).
3. Key APIs / services , main data stores , and online vs offline components.
4. Ranking approach (candidate generation + re-ranking), personalization signals, and dealing with constraints (delivery radius, store hours, availability, price, dietary needs).
5. Latency & reliability targets and strategies (caching, fallbacks, degradation).
6. Evaluation : offline metrics, online A/B metrics, and guardrails.

Assumptions to clarify

• You have restaurant metadata (location, cuisine, menu items, prices), user history, and real-time availability.
• Traffic is large (e.g., millions of daily users). Typical p95 latency target for search/recs is ~200–500ms.