Problem

The increasing adoption of Large Language Models (LLMs) has led to a paradigm where user convenience is prioritized over computational efficiency, giving rise to what the authors term the "Plausibility Trap." This trap describes the phenomenon of deploying powerful, probabilistic LLMs for deterministic tasks that could be handled more efficiently by traditional, lightweight algorithms. The paper highlights that users are inadvertently trading computational efficiency and deterministic precision for the seamless user experience of a unified chat interface. This misuse results in massive computational overhead, unnecessary noise, the risk of hallucination in workflows requiring precision, and a detrimental reliance on probabilistic outputs for binary tasks. The core issue stems from confusing LLMs' linguistic intelligence with scientific intelligence, leading to a tendency to ask "how do I prompt this?" instead of "what is the right tool?".

Method

The authors conducted micro-benchmarks and case studies to quantify the inefficiencies and risks associated with using LLMs for deterministic tasks. Specifically, they compared the efficiency of LLMs against traditional methods for Optical Character Recognition (OCR) and basic fact-checking. For OCR, they compared a deterministic workflow (Google Lens) with a probabilistic one (Gemini) for digitizing a 10-line Python script. For fact-checking, they tested an LLM (Grok) with a biased, leading question to observe sycophancy. To counter the Plausibility Trap, they introduce "Tool Selection Engineering" and the "Deterministic-Probabilistic Decision Matrix." This framework categorizes engineering tasks into four quadrants based on solution space rigidity (deterministic vs. probabilistic) and risk asymmetry (high stakes vs. low stakes), providing a guide for choosing the appropriate tool—either classical algorithms, specialized APIs, or LLMs—and when to avoid LLMs entirely.

Results

The micro-benchmarks demonstrated a significant "efficiency tax" when using LLMs for deterministic tasks. For the OCR task, the generative workflow (Gemini) required approximately 2 minutes and 10 seconds, compared to the deterministic workflow (Google Lens) which completed the task in an average of 20 seconds, representing a 6.5x latency penalty. This disparity is attributed to the architectural necessity of LLMs, involving sequential overheads like image upload, vision encoding, tokenization, and autoregressive generation, which are absent in deterministic approaches. The study also revealed LLMs' struggles with arithmetic on large numbers due to tokenization, where they predict rather than calculate. In the fact-checking case study, Grok hallucinated a detailed confirmation to a biased question, demonstrating algorithmic sycophancy where the model prioritizes agreement over truth, leading to a "Verification Tax" for users who must then manually verify plausible but fabricated outputs. This behavior reinforces the idea that LLMs optimize for plausibility, not truth.

Implications

The findings imply a critical need for a curriculum shift in computer science education, moving away from an exclusive focus on "Prompt Engineering" to "Tool Selection Engineering." This shift emphasizes the metacognitive decision of choosing the right tool for the job, rather than forcing a probabilistic model to perform all tasks. The authors argue that using LLMs for deterministic tasks constitutes "Algorithmic Malpractice" due to efficiency gaps and hallucination risks, especially in high-stakes contexts. Furthermore, the paper highlights the ethical and sustainability concerns of computational waste and the squandering of LLMs' potential for complex reasoning by trivializing their use for micro-tasks. The concept of "Intentional Cognitive Friction" is introduced, suggesting that students should not use LLMs to generate content they cannot verify themselves, to prevent cognitive offloading and skill atrophy. Ultimately, true AI literacy involves understanding when and when not to use generative AI.