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I enjoyed this section in the LLM evals course reader. Btw 35% discount here: https://t.co/3botO05IlE It suggests three gulfs builders have to navigate when building AI systems. First, the Gulf of Comprehension. Don't assume we know our data; that's a trap. Real-world data is… https://t.co/6E8ngo05Do
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"text": "I enjoyed this section in the LLM evals course reader. Btw 35% discount here: https://t.co/1dnFYH9nxN \n\nIt suggests three gulfs builders have to navigate when building AI systems.\n\nFirst, the Gulf of Comprehension. Don't assume we know our data; that's a trap. Real-world data is complex and ambiguous. Bridge this gap with evaluation and monitoring—instead of unchecked assumptions—to find where your LLM fails.\n\nNext, the Gulf of Specification. We tend to underestimate the rigor needed to translate human intent into machine instructions. Take care in the formatting and context. Bridge this gap by iteratively refining prompts with continuous feedback and evals. \n\nThird, the Gulf of Generalization. Accept that models are imperfect and stochastic. Bridge this gap with resilient design—task decomposition, robust error-handling, and continuous tuning—to improve system reliability as much as realistically possible.\n\nI humbly propose a fourth: the Gulf of Model Capability. Know when the model itself is the bottleneck. Perhaps we should park the task aside for now and check if the next model release can solve is sufficient. When trying to improve performance, be clear about which gulf you need to cross. Each gulf will require a different solution, such as looking at your data, writing better prompts, engineering for stochasticity, or just checking back with the next model release. \n\nThanks @sh_reya and @HamelHusain for this master class!",
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