I Built an AI Pet Behavior Analyzer — Here's the Two-Stage LLM Pattern That Made It Work

Have you ever watched your dog stare at a wall for 20 minutes straight and thought: is something wrong, or is this just... vibes? I have. And that question led me down a rabbit hole of AI, behavioral science, and a lot of very opinionated veterinarians. Here's what I built — and more importantly, what it taught me about building AI apps that actually understand emotional nuance. The Problem: Pet Behavior Is Deeply Contextual Most AI apps handle clean, structured inputs: "Give me a recipe." "Summarize this PDF." Easy. Pet behavior is the opposite. It's: Non-verbal Highly contextual (a cat hiding can mean playful, scared, sick, or Tuesday) Owner-biased (we anthropomorphize everything ) Building MyPetTherapist forced me to solve a real LLM challenge: how do you extract signal from emotionally loaded, ambiguous human descriptions? The Architecture (Simplified) from openai import OpenAI import json client = OpenAI () SYSTEM_PROMPT = """ You are a veterinary behavioral analyst. Your job is t