How to Build Reliable AI Systems.
We've all been there: You open ChatGPT, drop a prompt. "Extract all emails from this sheet and categorize by sentiment." It gives you something close. You correct it, it apologizes, and gives you a ne
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Archit Mittal
I Automate Chaos — AI workflows, n8n, Claude, and open-source automation for businesses. Turning repetitive work into one-click systems.
Reliability in AI systems is the gap most teams discover only after deployment. The pattern I keep seeing is that teams treat LLM calls like deterministic API calls — no retries, no fallback strategies, no output validation. The most resilient architectures I've worked with treat every LLM response as untrusted input: structured output parsing, confidence thresholds, and graceful degradation when the model returns unexpected formats. Eval-driven development is another underrated practice — having a regression suite for prompt changes is as important as unit tests for code.