Show HN: LLM Sanity Checks – A practical guide to not over-engineering AI

I keep seeing teams use frontier models for tasks a regex or a 4B model could do cheaper and faster.

This repo is a collection of opinionated patterns and heuristics to help you rethink the architecture of your AI workflows and ease the decision-making process while ensuring maximum efficiency.

It covers:

- A decision tree for architectural sanity checks. - Tradeoffs between JSON and delimiter-separated output. - Patterns for cascading models (verifying small models before calling big ones).

Open to feedback on other anti-patterns you've seen in production.