I’ve been experimenting with a domain‑specific AI assistant for plant care and plant problem diagnosis.
It’s called Plantcoach — an intent‑driven pipeline where the LLM only rewrites facts, never invents them.

Technical repo:
https://github.com/Introgreen/plantcoach

How it works (short version)

• Intent recognition (care, problems, pests, toxicity, propagation, attribute‑matching queries)
• Natural language → structured JSON
• Domain search (knowledge base + structured attributes)
• LLM only used for wording, not content

Example internal JSON:

json

{ "intent": "care", "topic": "monstera", "symptoms": ["brown leaf edges"], "language": "en" } 

Where I’m unsure

Curious how others think about:

• Does this architecture scale as the domain grows
• Is JSON‑routing too rigid long‑term
• Should intent detection move to a small local model
• Is a hybrid rule‑based + LLM pipeline future‑proof
• How do you handle multilingual domain assistants
• Would agent‑based systems be better for niche domains

Example questions it handles

• “Why does my Monstera get brown leaf edges”
• “Which plants are safe for cats”
• “Find a plant for a dark living room”

Would love input from people building domain‑specific assistants.