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The Unreliability of LLMs and What Lies Ahead
The Unreliability of LLMs & What Lies Ahead
There are caveats (including how human baselines are estimated), but the core insight holds: even with strong feedback signals and tightly scoped tasks, LLMs still make small, consequential mistakes—making truly high-accuracy code output surprisingly difficult to achieve. Unlike approaches that strive to engineer out all AI variance, the second broad strategy for LLM product development accepts a degree of inherent model variability and focuses on managing its impact by integrating human verification into the process. What defines this approach Specialized, narrow-scope offerings with optimized playbooks: Real value and defensibility are typically unlocked when a provider identifies a narrow scope—a specific, well-defined problem or function that can be demonstrably and massively improved with AI.
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