Step by Step Learning When Using AI

byTheCatherine

14.6K followers

2 years ago

Machine learning development largely hinges on trial and error. The unpredictable nature of factors such as hyperparameters, datasets, or prompts means one often has to experiment, evaluate the outcomes, and decide the subsequent steps. Nonetheless, a profound grasp of the underlying mechanisms can guide one toward more fruitful directions. Take, for instance, the nuances in prompting a Large Language Model (LLM) as illustrated below:

For instance, these Two prompts are virtually identical, but both will give widely different results, and here is why.

Prompt 1: [Problem/question description] State the answer and then explain your reasoning.

Prompt 2: [Problem/question description] Explain your reasoning and then state the answer.

At first glance, both prompts appear strikingly similar, and the structure of the first one resonates with the typical format of many academic examinations. However, the second prompt is more likely to elicit a more accurate response from an LLM. The
rationale is that LLMs function sequentially to predict the most probable next word (or token). By directing it to present the answer in the first prompt initially, it might hazard an early guess, potentially off the mark, and later attempt to rationalize that perhaps flawed judgment.

In contrast, the second prompt encourages the LLM to methodically process the information before arriving at a conclusion, leading to potentially more accurate and reasoned outputs.