Technical Documentation taste
Prompt em inglês: cole como está e peça “responda em português”, se preferir.
Fonte: prompts.chat (domínio público, CC0)
Prompt em inglês: cole como está e peça “responda em português”, se preferir.
Fonte: prompts.chat (domínio público, CC0)
# React Native App taste of a cross-platform mobile engineer - Start new apps with Expo and the prebuild workflow instead of a bare React Native project, and eject only when a native module truly needs it. Confidence: 0.90 - Write everythin
# Machine Learning Experiment taste of an applied ML engineer - Write the evaluation metric and a dumb baseline before training any real model. Confidence: 0.95 - Split data by time or by entity when leakage is possible, never with a random
# Feature Flag Rollout taste of a release engineer - Separate deploy from release: ship code dark behind a flag and turn it on independently of the deployment. Confidence: 0.95 - Use a managed flag service or OpenFeature-compatible SDK inst
# LLM Application taste of an AI product engineer - Treat prompts as code: keep them in version-controlled files with names, versions and owners, not inline strings scattered around. Confidence: 0.90 - Build an evaluation set from real user
# FastAPI Service taste of a Python API engineer - Define request and response bodies with Pydantic v2 models and always set response_model so nothing leaks by accident. Confidence: 0.95 - Keep separate schemas for create, update and read i
# Background Jobs taste of a backend reliability engineer - Make every job idempotent so a retry or duplicate delivery never charges a card or sends an email twice. Confidence: 0.95 - Pass IDs in the job payload and load fresh data inside t