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

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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.

Visit product View on Hacker NewsJan 26, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

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