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Evals are the most critical element of AI product development. But there are many misconceptions. Metrics promoted by the eval vendors, like "hallucination" or "toxicity," are ineffective and often miss domain-specific issues. No wonder 85% of AI initiatives fail (Gartner,… https://t.co/eD1eyLBEL1
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"text": "Evals are the most critical element of AI product development.\n\nBut there are many misconceptions.\n\nMetrics promoted by the eval vendors, like \"hallucination\" or \"toxicity,\" are ineffective and often miss domain-specific issues.\n\nNo wonder 85% of AI initiatives fail (Gartner, 2024).\n\nIt turns out that the most successful AI teams take a completely different approach. Rather than starting from the top, they:\n\n- Look at actual data\n- Identify failure modes\n- Let app-specific metrics emerge bottom-up\n\nAnd obsess over measuring, learning, and iterating. \n\nIn particular, they see error analysis as the highest-ROI activity.\n\nThe process is straightforward. You look at the data, label LLM traces, and classify the errors into failure modes.\n\nNext, quantify them and repeat the process until no new significant failure modes appear (\"theoretical saturation\").\n\nNext, look at the errors. What was the root cause?\n\nIf your instructions were unclear or incomplete, try to fix the prompt.\n\nIf an LLM fails to apply your clear instructions correctly (generalize), consider implementing one of the two types of automatic evaluators:\n\n- Code-Based Evals: Fast, cheap code checks for rule-based checks (e.g., XML/SQL formatting, Regex)\n\n- LLM-as-Judge Evals: For complex or subjective checks; start with a simple binary scale: fail/pass\n\nEach automatic evaluator targets a single failure mode. \n\nAnd that's how you get app-specific AI metrics you were looking for.\n\nRemember that it's critical to:\n\n- Consider the cost and effort on implementing an evaluator, in particular, model-based\n- Align LLM-as-Judges with humans\n- Measure the alignment (in particular, TPR)\n- Keep humans in the loop\n- Never stop looking at data\n\nHope that helps!",
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