The Reliability Bottleneck of LLMs: Four Strategies for Building AI Products
This article explores the inherent unreliability of Large Language Models (LLMs) and its implications for building AI products. LLM outputs often deviate significantly from the intended result, and this unreliability is particularly pronounced in tasks involving multi-step actions and tool use. The authors argue that this core unreliability is unlikely to change significantly in the short to medium term. Four strategies for managing LLM variance are presented: systems operating without user verification (pursuing determinism or 'good enough' accuracy), and systems incorporating explicit verification steps (end-user verification or provider-level verification). Each strategy has its strengths, weaknesses, and applicable scenarios; the choice depends on team capabilities and objectives.
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