RAG without the hype: menus, not magic
Retrieval helps when answers must cite your dishes and allergen records, not the whole internet. It is not a substitute for a kitchen that confirms sesame.
RAG is useful when the problem is find the right dish or policy, not invent copy. You have specs, supplier notes, allergen matrices — and staff keep asking because search is bad or the PDF is from March.
The model alone guesses. RAG retrieves chunks from your corpus and answers from that material. Done well, fewer hallucinations about pricing, garnishes or procedures.
It fails when the knowledge is a swamp. Duplicate Drive folders, PDFs from 2019, contradictory FAQs. Garbage in, confidently plated garbage out. Do not let a model decide allergen facts.
Successful projects invest in hygiene: one canonical menu, sensible chunking, a human who knows the kitchen confirming high-risk answers. That is why Quiteful keeps allergens as structured data, not a chatbot vibe.
Use RAG for bounded, maintainable knowledge. Do not use it as a substitute for organising information you were already unwilling to maintain.
Common questions
- What is RAG in plain English for restaurants?
- The system searches your menu docs first, then asks a model to answer from that material — so it is less likely to invent a dish you do not serve.
- When does it fail?
- When the source is a stale PDF, duplicated spreadsheets or nobody owns updates. Then you automate confident wrong allergens at scale.