Expired50% confidenceFactExact time
The theoretical foundation of prompt engineering lies in the autoregressive generation characteristics of the Transformer architecture, where model output quality is highly dependent on the degree of structure in the input.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Harness Engineering: A Practical Guide to AI Programming — From Prompts to Mastery
bilibili图灵诸葛讲Java5/28/2026
Related Claims
UnverifiedIn the traditional Transformer architecture, the amount of computation per token is fixed, while Chain-of-Thought technology increases the effective computation by having the model generate intermediate reasoning steps.79% similarUnverifiedCurrent mainstream large language models adopt the autoregressive generation mechanism of the Transformer architecture, generating token by token from left to right, and cannot go back to modify already-output reasoning steps after generation.76% similarUnverifiedModern large models are based on the Transformer architecture, and generating each token requires one full forward pass over all the model's parameters.75% similarUnverifiedIn the Transformer architecture, there is an approximately linear relationship between model parameter count and inference latency.74% similarUnverifiedFew-shot prompting leverages the in-context learning capability of the Transformer architecture, allowing the model to infer the expected output format from in-context examples alone without updating its parameters.73% similar
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